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Prediction report on th Machine Learning Prediction Model with Power BI.htm
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</style><body><br></br><h1><div>Power BI Documentation</div></h1><br></br><hr></hr><h2><div>Generated by <a href="http://radacad.com/power-bi-helper">Power BI Helper</a> (Copyright © <a href="http://radacad.com">RADACAD</a>).</div><br></br><div>Date: 4/24/2023 2:58:39 AM</div><br></br></h2><h1><div>------------------****** Visualization ******---------------------</div></h1><br></br><h2><div>File: Prediction report for David ML[David ML].pbix</div></h2><br></br><h3><div>Path: D:\PP\Dataproanalytic\Learning Content\My Past Power BI Training Project\Prediction report for David ML[David ML].pbix</div></h2><br></br><hr></hr><br></br><h3><div>List of Pages:</div></h3><br></br><table border='1px' cellpadding='1'><tr ><td >DisplayName</td><td >Name</td><td >Ordinal</td><td >DisplayOption</td><td >Width</td><td >Height</td><td >PageIndex</td></tr><tr ><td >Model Performance</td><td >ReportSectiond9def7b9b30452bda054</td><td >0</td><td >3</td><td >1280</td><td >1400</td><td >0</td></tr><tr ><td 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>2355c18011abec4244be</td><td >22000</td><td >30</td><td >0</td></tr><tr ><td ></td><td ></td><td >100</td><td >328</td><td >18000</td><td >452</td><td >347</td><td >45dfef0b336a3327122d</td><td >33000</td><td >31</td><td >0</td></tr><tr ><td >card</td><td ></td><td >308</td><td >104</td><td >3000</td><td >144</td><td >120</td><td >c64bc0d42389313074be</td><td >28000</td><td >32</td><td >0</td></tr><tr ><td >card</td><td ></td><td >161</td><td >104</td><td >2000</td><td >144</td><td >120</td><td >124600d75790a3d4dda3</td><td >29000</td><td >33</td><td >0</td></tr><tr ><td >card</td><td ></td><td >161</td><td >227</td><td >0</td><td >144</td><td >120</td><td >3021c253d013b0b156d4</td><td >30000</td><td >34</td><td >0</td></tr><tr ><td >card</td><td ></td><td >308</td><td >227</td><td >1000</td><td >144</td><td >120</td><td >f73c6490d0b9a859e83d</td><td >27000</td><td >35</td><td >0</td></tr><tr ><td >card</td><td ></td><td >0</td><td >227</td><td >6000</td><td >157</td><td >120</td><td >8c8d3888dc42c237bc30</td><td >33000</td><td >36</td><td >0</td></tr><tr ><td >card</td><td ></td><td >0</td><td >104</td><td >4000</td><td >157</td><td >120</td><td >115282e137dd8adc00d8</td><td >31000</td><td >37</td><td >0</td></tr><tr ><td >card</td><td ></td><td >161</td><td >0</td><td >7000</td><td >145</td><td >97</td><td >11f03a46388b66946370</td><td >20000</td><td >38</td><td >0</td></tr><tr ><td >card</td><td ></td><td >308</td><td >0</td><td >5000</td><td >144</td><td >97</td><td >89fcb3c37e1ed5c03103</td><td >32000</td><td >39</td><td >0</td></tr><tr ><td ></td><td ></td><td >583</td><td >377</td><td >26000</td><td >630</td><td >1023</td><td >bb14a2fa273fd1269dfa</td><td >52000</td><td >40</td><td >0</td></tr><tr ><td >tableEx</td><td >'How the model was tested'</td><td >37</td><td >423</td><td >1000</td><td >248</td><td >543</td><td >9d8dd0957905504be349</td><td >45000</td><td >41</td><td >0</td></tr><tr ><td >textbox</td><td ></td><td >37</td><td >106</td><td >2000</td><td >235</td><td >319</td><td >0af500d94fec6945334c</td><td >46000</td><td >42</td><td >0</td></tr><tr ><td >textbox</td><td ></td><td >306</td><td >549</td><td >5000</td><td >299</td><td >90</td><td >b77a84ddf68163c89f2c</td><td >47000</td><td >43</td><td >0</td></tr><tr ><td >tableEx</td><td >'How the model was tested'</td><td >306</td><td >577</td><td >6000</td><td >300</td><td >385</td><td >54476d9ba85c7258af6a</td><td >48000</td><td >44</td><td >0</td></tr><tr ><td >textbox</td><td ></td><td >313</td><td >48</td><td >7000</td><td >212</td><td >99</td><td >8e07cf357c69d66fff01</td><td >49000</td><td >45</td><td >0</td></tr><tr ><td >tableEx</td><td ></td><td >308</td><td >75</td><td >8500</td><td >299</td><td >437</td><td >bf88a386ec1c2972558b</td><td >50000</td><td >46</td><td >0</td></tr><tr ><td >textbox</td><td ></td><td >38</td><td >48</td><td >9000</td><td >277</td><td >86</td><td >02a7e87b8ddc3456e5e2</td><td >51000</td><td >47</td><td >0</td></tr><tr ><td >basicShape</td><td ></td><td >0</td><td >0</td><td >0</td><td >630</td><td >1023</td><td >d4abf5a0a3f9504aaef6</td><td >52000</td><td >48</td><td >0</td></tr><tr ><td >multiRowCard</td><td >'Final Model'</td><td >647</td><td >254</td><td >19000</td><td >123</td><td >71</td><td >30e27e27c05e290c1c69</td><td >34000</td><td >49</td><td >0</td></tr><tr ><td >textbox</td><td ></td><td >605</td><td >173</td><td >20000</td><td >183</td><td >37</td><td >0bda4399acc57ed2a372</td><td >35000</td><td >50</td><td >0</td></tr><tr ><td >actionButton</td><td >'Show Text'</td><td >1149</td><td >358</td><td >21000</td><td >41</td><td >39</td><td >b0eddf547b7f387fc93b</td><td >36000</td><td >51</td><td >0</td></tr><tr ><td >actionButton</td><td >'ShowCBTextButton'</td><td >798</td><td >760</td><td >22000</td><td >100</td><td >40</td><td >e21011c2bce28b6de55f</td><td >37000</td><td >52</td><td >0</td></tr><tr ><td >image</td><td >'Close image'</td><td >1151</td><td >397</td><td >27000</td><td >39</td><td >27</td><td >01f75d25de6b01e4916f</td><td >39000</td><td >53</td><td >0</td></tr><tr ><td >image</td><td >'Close image'</td><td >1160</td><td >808</td><td >28000</td><td >30</td><td >25</td><td >bbb154501e8138c782ff</td><td >40000</td><td >54</td><td >0</td></tr><tr ><td >actionButton</td><td >'Close button main text'</td><td >1151</td><td >397</td><td >29000</td><td >44</td><td >28</td><td >c347c330b266ff6c9844</td><td >41000</td><td >55</td><td >0</td></tr><tr ><td >actionButton</td><td >'Close button'</td><td >1166</td><td >808</td><td >30000</td><td >24</td><td >25</td><td >e75bb8b387641449f6fb</td><td >42000</td><td >56</td><td >0</td></tr></table><br></br><h3><div>Visuals in Accuracy Report:</div></h3><table border='1px' cellpadding='1'><tr ><td >Visual Type</td><td >Title</td><td >X</td><td >Y</td><td >Z</td><td >Width</td><td >Height</td><td >Name</td><td >tabOrder</td><td >VisualIndex</td><td >PageIndex</td></tr><tr ><td >lineChart</td><td >'Cumulative Gains Chart'</td><td >66</td><td >884</td><td >1000</td><td >560</td><td >473</td><td >749ac7b02e1eb0c97020</td><td ></td><td >0</td><td >1</td></tr><tr ><td >areaChart</td><td >'ROC Curve'</td><td >638</td><td >884</td><td >2000</td><td >560</td><td >473</td><td >7d76b937c4ab612c0268</td><td >1000</td><td >1</td><td >1</td></tr><tr ><td >textbox</td><td ></td><td >64</td><td >392</td><td >3000</td><td >552</td><td >432</td><td >23fa2514d04e167a61e7</td><td >2000</td><td >2</td><td >1</td></tr><tr ><td >textbox</td><td ></td><td >640</td><td >392</td><td >4000</td><td >576</td><td >432</td><td >8f847c658ae0c0dae04d</td><td >3000</td><td >3</td><td >1</td></tr><tr ><td >textbox</td><td ></td><td >80</td><td >39</td><td >5000</td><td >233</td><td >83</td><td >f9d816b32cc2ae827930</td><td >4000</td><td >4</td><td >1</td></tr><tr ><td >basicShape</td><td ></td><td >54</td><td >42</td><td >6000</td><td >33</td><td >71</td><td >1bfaa5b467daed91847a</td><td >5000</td><td >5</td><td >1</td></tr><tr ><td >basicShape</td><td ></td><td >63</td><td >140</td><td >7000</td><td >1152</td><td >196</td><td >a13f539aa984981a0322</td><td >6000</td><td >6</td><td >1</td></tr><tr ><td >multiRowCard</td><td >'Final Model'</td><td >826</td><td >210</td><td >8000</td><td >136</td><td >70</td><td >e3870901051776eab05b</td><td >7000</td><td >7</td><td >1</td></tr><tr ><td >textbox</td><td ></td><td >780</td><td >178</td><td >9000</td><td >228</td><td >48</td><td >2379bc92d08245eb7ad4</td><td >8000</td><td >8</td><td >1</td></tr><tr ><td >textbox</td><td ></td><td >100</td><td >177</td><td >10000</td><td >425</td><td >47</td><td >9e302d2c5b71b7140901</td><td >9000</td><td >9</td><td >1</td></tr><tr ><td >basicShape</td><td ></td><td >64</td><td >201</td><td >0</td><td >25</td><td >25</td><td >b0578dc2f256d7be2aa6</td><td >10000</td><td >10</td><td >1</td></tr><tr ><td >textbox</td><td ></td><td >100</td><td >210</td><td >11000</td><td >453</td><td >94</td><td >9bd577cb76003dd728a0</td><td >11000</td><td >11</td><td >1</td></tr></table><br></br><h3><div>Visuals in Training Details:</div></h3><table border='1px' cellpadding='1'><tr ><td >Visual Type</td><td >Title</td><td >X</td><td >Y</td><td >Z</td><td >Width</td><td >Height</td><td >Name</td><td >tabOrder</td><td >VisualIndex</td><td >PageIndex</td></tr><tr ><td >basicShape</td><td ></td><td >64</td><td >800</td><td >3000</td><td >1152</td><td >49</td><td >33c051cfb91b99052a0b</td><td ></td><td >0</td><td >2</td></tr><tr ><td >donutChart</td><td ></td><td >155</td><td >1933</td><td >0</td><td >939</td><td >567</td><td >2cd8d9a4d2559e01a850</td><td >1000</td><td >1</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >80</td><td >39</td><td >5000</td><td >233</td><td >83</td><td >957438d341bd67ba9dc7</td><td >2000</td><td >2</td><td >2</td></tr><tr ><td >basicShape</td><td ></td><td >54</td><td >42</td><td >6000</td><td >33</td><td >72</td><td >80a2411131d4300d0c1c</td><td >3000</td><td >3</td><td >2</td></tr><tr ><td >basicShape</td><td ></td><td >64</td><td >140</td><td >7000</td><td >1152</td><td >240</td><td >d6ad084d1c0a19344897</td><td >4000</td><td >4</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >105</td><td >180</td><td >8000</td><td >244</td><td >47</td><td >9d0d4de5e9a03c079ee8</td><td >5000</td><td >5</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >592</td><td >204</td><td >9000</td><td >144</td><td >32</td><td >2b4522d9005638dc700c</td><td >6000</td><td >6</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >592</td><td >262</td><td >10000</td><td >144</td><td >32</td><td >9308b70c97d8a0808511</td><td >7000</td><td >7</td><td >2</td></tr><tr ><td >multiRowCard</td><td >'Number Of Iterations'</td><td >744</td><td >207</td><td >11000</td><td >120</td><td >31</td><td >b5d1fc4813ae60ecb4c1</td><td >8000</td><td >8</td><td >2</td></tr><tr ><td >multiRowCard</td><td >'Number Of Iterations'</td><td >744</td><td >261</td><td >12000</td><td >120</td><td >32</td><td >724acea9cc1a03061472</td><td >9000</td><td >9</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >827</td><td >205</td><td >13000</td><td >157</td><td >33</td><td >9d68b9b45d0eda00c301</td><td >10000</td><td >10</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >827</td><td >259</td><td >14000</td><td >144</td><td >36</td><td >77878dc708c8a6b14a04</td><td >11000</td><td >11</td><td >2</td></tr><tr ><td >multiRowCard</td><td >'Final Model'</td><td >976</td><td >205</td><td >15000</td><td >200</td><td >55</td><td >c62a64e0702e9c1ac45d</td><td >12000</td><td >12</td><td >2</td></tr><tr ><td >multiRowCard</td><td >'Final Model'</td><td >976</td><td >262</td><td >16000</td><td >120</td><td >34</td><td >0f6ed5ac05306a19e9a8</td><td >13000</td><td >13</td><td >2</td></tr><tr ><td >lineChart</td><td >'Model quality over iterations'</td><td >64</td><td >421</td><td >17000</td><td >1152</td><td >377</td><td >cc385b0d0a58b0ed1c44</td><td >14000</td><td >14</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >64</td><td >840</td><td >18000</td><td >1152</td><td >132</td><td >776564edd04561e04269</td><td >15000</td><td >15</td><td >2</td></tr><tr ><td >tableEx</td><td >'Data Featurization'</td><td >64</td><td >1024</td><td >19000</td><td >560</td><td >632</td><td >1bee6ee01b86082919dc</td><td >16000</td><td >16</td><td >2</td></tr><tr ><td >tableEx</td><td >'Final Parameters Selected'</td><td >656</td><td >1024</td><td >20000</td><td >552</td><td >584</td><td >8856091cdc25ca202434</td><td >17000</td><td >17</td><td >2</td></tr><tr ><td >basicShape</td><td ></td><td >64</td><td >1680</td><td >4000</td><td >1152</td><td >49</td><td >a969da2ff1719a08268a</td><td >18000</td><td >18</td><td >2</td></tr><tr ><td >EnsembleMachineLearningModelsInfoTextBoxA9CD31B0C27540AFA7440DBBFBB714F6</td><td ></td><td >64</td><td >1718</td><td >1000</td><td >1152</td><td >232</td><td >187a95c0441a955d0e09</td><td >19000</td><td >19</td><td >2</td></tr><tr ><td >clusteredColumnChart</td><td ></td><td >80</td><td >2345</td><td >2000</td><td >501</td><td >155</td><td >18dd3e36c80e068aa00a</td><td >20000</td><td >20</td><td >2</td></tr><tr ><td >textbox</td><td ></td><td >105</td><td >215</td><td >21000</td><td >487</td><td >128</td><td >7e65983420619ab9695c</td><td >21000</td><td >21</td><td >2</td></tr></table><hr></hr><br></br><h3><div>List of Bookmarks:</div></h3><br></br><table border='1px' cellpadding='1'><tr ><td >DisplayName</td><td >Page Name</td><td >Index</td><td >Name</td></tr><tr ><td >KeyInfluencersShown</td><td >ReportSectiond9def7b9b30452bda054</td><td >0</td><td >Bookmark43da22d2da50a011020d</td></tr><tr ><td >KeyInfluencersHidden</td><td >ReportSectiond9def7b9b30452bda054</td><td >1</td><td >Bookmark746e63b061188096802b</td></tr><tr ><td >Show Text</td><td >ReportSectiond9def7b9b30452bda054</td><td >2</td><td >Bookmark43e28b1d7244530e64ef</td></tr><tr ><td >Hide Text</td><td >ReportSectiond9def7b9b30452bda054</td><td >3</td><td >Bookmarkec9078b0f1c230606202</td></tr><tr ><td >CBTextHidden</td><td >ReportSectiond9def7b9b30452bda054</td><td >4</td><td >Bookmarkdceafd0ceaed809248b3</td></tr><tr ><td >CBTextShown</td><td >ReportSectiond9def7b9b30452bda054</td><td >5</td><td >Bookmark130bdf9c05d1a6b9cb72</td></tr></table><hr></hr><br></br><h3><div>List of all Columns/Fields/Measures/Expressions Used in Visuals:</div></h3><br></br><table border='1px' cellpadding='1' cellspacing='1'><tr ><td >Name</td><td >Table Name</td><td >Aggregation</td><td >Expression</td><td >VisualIndex</td><td >PageIndex</td></tr><tr ><td >PrecisionSubtext</td><td >Output</td><td ></td><td >Output.PrecisionSubtext</td><td >3</td><td >0</td></tr><tr ><td >Precision</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Precision</td><td >4</td><td >0</td></tr><tr ><td >Recall</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Recall</td><td >5</td><td >0</td></tr><tr ><td >RecallSubtext</td><td >Output</td><td ></td><td >Output.RecallSubtext</td><td >6</td><td >0</td></tr><tr ><td >Bin</td><td >KeyInfluencers_Breakdowns</td><td ></td><td >KeyInfluencers_Breakdowns.Bin</td><td >10</td><td >0</td></tr><tr ><td ></td><td >KeyInfluencers_Breakdowns</td><td >Sum</td><td >Sum(KeyInfluencers_Breakdowns.% of Positive Outcome)</td><td >10</td><td >0</td></tr><tr ><td >Bin</td><td >KeyInfluencers_Breakdowns</td><td ></td><td >KeyInfluencers_Breakdowns.Bin</td><td >10</td><td >0</td></tr><tr ><td >Feature Importance</td><td >KeyInfluencers_TopPredictors</td><td >Sum</td><td >Sum(KeyInfluencers_TopPredictors.Feature Importance)</td><td >12</td><td >0</td></tr><tr ><td >Feature Name</td><td >KeyInfluencers_TopPredictors</td><td ></td><td >KeyInfluencers_TopPredictors.Feature Name</td><td >12</td><td >0</td></tr><tr ><td >Feature Name</td><td >KeyInfluencers_TopPredictors</td><td ></td><td >KeyInfluencers_TopPredictors.Feature Name</td><td >12</td><td >0</td></tr><tr ><td >ModelTested</td><td >Output</td><td ></td><td >Output.ModelTested</td><td >18</td><td >0</td></tr><tr ><td >Confidence Threshold</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Confidence Threshold</td><td >22</td><td >0</td></tr><tr ><td >Measure</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Measure</td><td >22</td><td >0</td></tr><tr ><td >Population</td><td >ProbabilityDistribution</td><td >CountNonNull</td><td >CountNonNull(ProbabilityDistribution.Population)</td><td >22</td><td >0</td></tr><tr ><td >Cost</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Cost</td><td >22</td><td >0</td></tr><tr ><td >Confidence Threshold</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Confidence Threshold</td><td >22</td><td >0</td></tr><tr ><td >PopulationSize</td><td >PopulationSize</td><td ></td><td >PopulationSize.PopulationSize</td><td >24</td><td >0</td></tr><tr ><td >PopulationSize</td><td >PopulationSize</td><td ></td><td >PopulationSize.PopulationSize</td><td >24</td><td >0</td></tr><tr ><td >UnitCost</td><td >UnitCost</td><td ></td><td >UnitCost.UnitCost</td><td >25</td><td >0</td></tr><tr ><td >UnitCost</td><td >UnitCost</td><td ></td><td >UnitCost.UnitCost</td><td >25</td><td >0</td></tr><tr ><td >UnitBenefit</td><td >UnitBenefit</td><td ></td><td >UnitBenefit.UnitBenefit</td><td >26</td><td >0</td></tr><tr ><td >UnitBenefit</td><td >UnitBenefit</td><td ></td><td >UnitBenefit.UnitBenefit</td><td >26</td><td >0</td></tr><tr ><td >CostBenefitAnalysis</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.CostBenefitAnalysis</td><td >28</td><td >0</td></tr><tr ><td >Confidence Threshold</td><td >SliderValue</td><td ></td><td >SliderValue.Confidence Threshold</td><td >30</td><td >0</td></tr><tr ><td >Confidence Threshold</td><td >SliderValue</td><td ></td><td >SliderValue.Confidence Threshold</td><td >30</td><td >0</td></tr><tr ><td >OutcomeDistributionValue</td><td >ProbabilityDistributionUnpivot</td><td ></td><td >ProbabilityDistributionUnpivot.OutcomeDistributionValue</td><td >32</td><td >0</td></tr><tr ><td >OutcomeDistributionValue</td><td >ProbabilityDistributionUnpivot</td><td ></td><td >ProbabilityDistributionUnpivot.OutcomeDistributionValue</td><td >33</td><td >0</td></tr><tr ><td >OutcomeDistributionValue</td><td >ProbabilityDistributionUnpivot</td><td ></td><td >ProbabilityDistributionUnpivot.OutcomeDistributionValue</td><td >34</td><td >0</td></tr><tr ><td >OutcomeDistributionValue</td><td >ProbabilityDistributionUnpivot</td><td ></td><td >ProbabilityDistributionUnpivot.OutcomeDistributionValue</td><td >35</td><td >0</td></tr><tr ><td >MatrixActualFalse</td><td >Output</td><td ></td><td >Output.MatrixActualFalse</td><td >36</td><td >0</td></tr><tr ><td >MatrixActualTrue</td><td >Output</td><td ></td><td >Output.MatrixActualTrue</td><td >37</td><td >0</td></tr><tr ><td >MatrixPredictedTrue</td><td >Output</td><td ></td><td >Output.MatrixPredictedTrue</td><td >38</td><td >0</td></tr><tr ><td >MatrixPredictedFalse</td><td >Output</td><td ></td><td >Output.MatrixPredictedFalse</td><td >39</td><td >0</td></tr><tr ><td >InterpretResultsDynamicText</td><td >Output</td><td ></td><td >Output.InterpretResultsDynamicText</td><td >41</td><td >0</td></tr><tr ><td >DecideApplyDynamicText</td><td >Output</td><td ></td><td >Output.DecideApplyDynamicText</td><td >44</td><td >0</td></tr><tr ><td >Precision and recall</td><td >Output</td><td ></td><td >Output.Precision and recall</td><td >46</td><td >0</td></tr><tr ><td >Area under ROC curve</td><td >Output</td><td ></td><td >Output.Area under ROC curve</td><td >49</td><td >0</td></tr><tr ><td >IdealModel</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.IdealModel</td><td >0</td><td >1</td></tr><tr ><td >Random guess</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Random guess</td><td >0</td><td >1</td></tr><tr ><td >Model</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Model</td><td >0</td><td >1</td></tr><tr ><td >Population</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Population</td><td >0</td><td >1</td></tr><tr ><td >Confidence Threshold</td><td >ProbabilityDistribution</td><td >CountNonNull</td><td >CountNonNull(ProbabilityDistribution.Confidence Threshold)</td><td >0</td><td >1</td></tr><tr ><td >Population</td><td >ProbabilityDistribution</td><td ></td><td >ProbabilityDistribution.Population</td><td >0</td><td >1</td></tr><tr ><td >FPR</td><td >ROC</td><td ></td><td >ROC.FPR</td><td >1</td><td >1</td></tr><tr ><td >ModelROC</td><td >ROC</td><td ></td><td >ROC.ModelROC</td><td >1</td><td >1</td></tr><tr ><td >FPR</td><td >ROC</td><td >Min</td><td >Min(ROC.FPR)</td><td >1</td><td >1</td></tr><tr ><td >FPR</td><td >ROC</td><td >Sum</td><td >Sum(ROC.FPR)</td><td >1</td><td >1</td></tr><tr ><td >FPR</td><td >ROC</td><td ></td><td >ROC.FPR</td><td >1</td><td >1</td></tr><tr ><td >Area under ROC curve</td><td >Output</td><td ></td><td >Output.Area under ROC curve</td><td >7</td><td >1</td></tr><tr ><td >Algorithm Details</td><td >EnsembleIterationFullDetails</td><td ></td><td >EnsembleIterationFullDetails.Algorithm Details</td><td >1</td><td >2</td></tr><tr ><td >Weights</td><td >EnsembleIterationFullDetails</td><td >Sum</td><td >Sum(EnsembleIterationFullDetails.Weights)</td><td >1</td><td >2</td></tr><tr ><td >AlgorithmKey</td><td >EnsembleIterationFullDetails</td><td ></td><td >EnsembleIterationFullDetails.AlgorithmKey</td><td >1</td><td >2</td></tr><tr ><td >Algorithm Details</td><td >EnsembleIterationFullDetails</td><td ></td><td >EnsembleIterationFullDetails.Algorithm Details</td><td >1</td><td >2</td></tr><tr ><td >Total Data Sampled</td><td >Output</td><td ></td><td >Output.Total Data Sampled</td><td >8</td><td >2</td></tr><tr ><td >Training Data</td><td >Output</td><td ></td><td >Output.Training Data</td><td >9</td><td >2</td></tr><tr ><td >Algorithm Selected</td><td >IterationDetails</td><td ></td><td >IterationDetails.Algorithm Selected</td><td >12</td><td >2</td></tr><tr ><td >Numbe Of Iterations</td><td >Output</td><td ></td><td >Output.Numbe Of Iterations</td><td >13</td><td >2</td></tr><tr ><td >Index</td><td >IterationDetails</td><td ></td><td >IterationDetails.Index</td><td >14</td><td >2</td></tr><tr ><td >Score</td><td >IterationDetails</td><td >Sum</td><td >Sum(IterationDetails.Score)</td><td >14</td><td >2</td></tr><tr ><td >Estimator Name</td><td >IterationDetails</td><td >Min</td><td >Min(IterationDetails.Estimator Name)</td><td >14</td><td >2</td></tr><tr ><td >Index</td><td >IterationDetails</td><td ></td><td >IterationDetails.Index</td><td >14</td><td >2</td></tr><tr ><td >Feature</td><td >Featurizer</td><td ></td><td >Featurizer.Feature</td><td >16</td><td >2</td></tr><tr ><td >Detected Column Type</td><td >Featurizer</td><td ></td><td >Featurizer.Detected Column Type</td><td >16</td><td >2</td></tr><tr ><td >Imputation</td><td >Featurizer</td><td ></td><td >Featurizer.Imputation</td><td >16</td><td >2</td></tr><tr ><td >1</td><td ></td><td ></td><td >IterationDetailsMax.Value.1</td><td >17</td><td >2</td></tr><tr ><td >2</td><td ></td><td ></td><td >IterationDetailsMax.Value.2</td><td >17</td><td >2</td></tr><tr ><td >Measure</td><td >IsEnsemble</td><td ></td><td >IsEnsemble.Measure</td><td >19</td><td >2</td></tr><tr ><td >Measure</td><td >IsEnsemble</td><td ></td><td >IsEnsemble.Measure</td><td >20</td><td >2</td></tr></table><hr></hr><br></br><h3><div>List of Tables Used in Visuals:</div></h3><br></br><table border='1px' cellpadding='1' cellspacing='1'><tr ><td >Name</td></tr><tr ><td >Output</td></tr><tr ><td >PopulationSize</td></tr><tr ><td >UnitCost</td></tr><tr ><td >UnitBenefit</td></tr><tr ><td >SliderValue</td></tr><tr ><td >ProbabilityDistribution</td></tr><tr ><td >KeyInfluencers_Breakdowns</td></tr><tr ><td >KeyInfluencers_TopPredictors</td></tr><tr ><td >ProbabilityDistributionUnpivot</td></tr><tr ><td >ROC</td></tr><tr ><td >EnsembleIterationFullDetails</td></tr><tr ><td >IterationDetails</td></tr><tr ><td >Featurizer</td></tr><tr ><td >IterationDetailsMax</td></tr><tr ><td >IsEnsemble</td></tr></table><hr></hr><br></br><h3><div>List of Columns Not Used in Visuals:</div></h3><br></br><table border='1px' cellpadding='1' cellspacing='1'><tr ><td >Name</td><td >Table Name</td><td >State</td><td >Data Category</td><td >Data Type</td><td >Description</td><td >Display Folder</td><td >Error Message</td><td >Format String</td><td >Is Hidden</td><td >Modified Time</td><td >Structure Modified Time</td><td >Sort by Column</td><td >Summarize By</td><td >Type</td><td >Expression</td></tr><tr ><td >Actual</td><td >Distribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Predicted</td><td >Distribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Count1</td><td >Distribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Actual</td><td >DataTableDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Predicted</td><td >DataTableDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Counts</td><td >DataTableDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Name</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Value</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Confidence</td><td >PositiveDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Count</td><td >PositiveDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Confidence</td><td >NegativeDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Count</td><td >NegativeDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Precision Check</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Recall Check</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Selectivity Check</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Accuracy Check</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >TP</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >FP</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >TN</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >FN</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >LabelForTrueOutcomes</td><td >LabelForTrueOutcomes</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >LabelForFalseOutcomes</td><td >LabelForFalseOutcomes</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >ParameterName</td><td >Parameters</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >ParameterValue</td><td >Parameters</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >WorkspaceId</td><td >WorkspaceId</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >DataflowId</td><td >DataflowId</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >EntityName</td><td >EntityName</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >LabelColumnName</td><td >LabelColumnName</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Details</td><td >Featurizer</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Transformations</td><td >Featurizer</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Predicted</td><td >ProbabilityDistributionUnpivot</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Value</td><td >ProbabilityDistributionUnpivot</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Actual</td><td >ProbabilityDistributionUnpivot</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Confidence Threshold</td><td >ProbabilityDistributionUnpivot</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >TPR</td><td >ROC</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Estimator</td><td >IterationDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Scaler</td><td >IterationDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Parameter Name</td><td >IterationDetailsMax</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Parameter Value</td><td >IterationDetailsMax</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Index</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Iteration</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >EnsembleIterationAlgorithmNames.Index</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >EnsembleIterationAlgorithmNames.Details.pipeline.1</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Algorithm Score</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Algorithm Name</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Algorithm Parameter Name</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Algorithm Parameter Value</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >IsEnsemble</td><td >IsEnsemble</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Confidence Threshold</td><td >Selector Table</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Feature Type</td><td >KeyInfluencers_TopPredictors</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >Feature Name</td><td >KeyInfluencers_Breakdowns</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >% of Positive Outcome</td><td >KeyInfluencers_Breakdowns</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >COLUMN</td><td ></td></tr><tr ><td >10KScenarioStatement</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >10KPrecisionStatement</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >10KRecallStatement</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >10KActualWins</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >10KPredictedWins</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >10KTruePositiveWins</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >PositivePredictorHeading</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >PositivePredictorBreakdownHeader</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Input row count</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Sampled row count</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Training row count</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Validation row count</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >TrainingSubtitleThreshold</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Benefit Analysis Title</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Cost Analysis Title</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Pipeline Steps</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Model Accuracy</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >ModelTraining</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Final Model Quality</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >ModelParametersTitle</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Precision recall text</td><td >Output</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >False Negative</td><td >PositiveDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >True Positive</td><td >PositiveDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >False Positive</td><td >NegativeDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >True Negative</td><td >NegativeDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Selectivity</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Accuracy</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Profit</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >MaxProfit</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >TargetThreshold</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >ThresholdForMaxProfit</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >PopulationPercentageForMaxProfit</td><td >ProbabilityDistribution</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >FalseLabel</td><td >Parameters</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >TrueLabel</td><td >Parameters</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >LabelColumnName</td><td >Parameters</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Final Algorithm Selected</td><td >IterationDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >AlgorithmParamValue</td><td >EnsembleIterationFullDetails</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >selected</td><td >Selector Table</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >IsTopPredictorSelected</td><td >KeyInfluencers_Breakdowns</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr><tr ><td >Chart title</td><td >KeyInfluencers_Breakdowns</td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td ></td><td >MEASURE</td><td ></td></tr></table><hr></hr><br></br><h3><div>List of Tables Not Used in Visuals:</div></h3><br></br><table border='1px' cellpadding='1' cellspacing='1'><tr ><td >Name</td></tr><tr ><td >Distribution</td></tr><tr ><td >DataTableDistribution</td></tr><tr ><td >PositiveDistribution</td></tr><tr ><td >NegativeDistribution</td></tr><tr ><td >LabelForTrueOutcomes</td></tr><tr ><td >LabelForFalseOutcomes</td></tr><tr ><td >Parameters</td></tr><tr ><td >WorkspaceId</td></tr><tr ><td >DataflowId</td></tr><tr ><td >EntityName</td></tr><tr ><td >LabelColumnName</td></tr><tr ><td >Selector Table</td></tr></table><h1><div>------------------****** Model ******---------------------</div></h1><br></br><h2><div>Model: Prediction report for David ML[David ML]</div></h2><br></br><hr></hr><br></br><h3><div>List of Tables:</div></h3><br></br><table border='1px' cellpadding='1' cellspacing='1'><tr ><td >Name</td><td >Description</td><td >Storage Mode</td><td >Source</td><td >Is Hidden</td></tr><tr ><td >DateTableTemplate_bf74721a-aede-42c8-8889-ff64aefef54c</td><td ></td><td >Import</td><td ><pre>Calendar(Date(2015,1,1), Date(2015,1,1))</pre></td><td >True</td></tr><tr ><td >SliderValue</td><td ></td><td >Import</td><td ><pre>SUMMARIZE(ProbabilityDistribution,ProbabilityDistribution[Confidence Threshold])</pre></td><td >False</td></tr><tr ><td >Distribution</td><td ></td><td >Import</td><td ><pre>
VAR selectedValue = MAX(SliderValue[Confidence Threshold])
VAR TrueP = CALCULATE(AVERAGE(ProbabilityDistribution[TP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseP = CALCULATE(AVERAGE(ProbabilityDistribution[FP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR TrueN = CALCULATE(AVERAGE(ProbabilityDistribution[TN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseN = CALCULATE(AVERAGE(ProbabilityDistribution[FN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
return SELECTCOLUMNS({("Actual Win", "True Positive", TrueP), ("Actual Win", "False Negative", FalseN), ("Actual Loss", "True Negative", TrueN), ("Actual Loss", "False Positive", FalseP)}, "Actual", [Value1], "Predicted", [Value2], "Count", [Value3])</pre></td><td >False</td></tr><tr ><td >DataTableDistribution</td><td ></td><td >Import</td><td ><pre>
DATATABLE("Actual", string, "Predicted", string, {{"Actual Win", "True Positive"}, {"Actual Win", "False Negative"}, {"Actual Loss", "True Negative"}, {"Actual Loss", "False Positive"}})</pre></td><td >False</td></tr><tr ><td >Output</td><td ></td><td >Import</td><td ><pre>let
Source = PowerBI.Dataflows([]),
Workspace = Source{[workspaceId=WorkspaceId]}[Data],
Dataflow = Workspace{[dataflowId=DataflowId]}[Data],
ModelOutput = Dataflow{[entity=EntityName]}[Data],
TrainingResult = ModelOutput{0}[Stats],
data = Json.Document(TrainingResult as any) as any
in
data</pre></td><td >False</td></tr><tr ><td >PositiveDistribution</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
positiveScoringDistribution = Output[Reports][ScoringDistribution][Positive],
ConvertedTable = Table.FromRows(positiveScoringDistribution, {"Confidence", "Count"}),
#"Changed Type" = Table.TransformColumnTypes(ConvertedTable, {{"Confidence", type number}, {"Count", Int64.Type}})
in
#"Changed Type"</pre></td><td >False</td></tr><tr ><td >NegativeDistribution</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
negativeScoringDistribution = Output[Reports][ScoringDistribution][Negative],
ConvertedTable = Table.FromRows(negativeScoringDistribution, {"Confidence","Count"}),
#"Changed Type" = Table.TransformColumnTypes(ConvertedTable,{{"Confidence", type number}, {"Count", Int64.Type}})
in
#"Changed Type"</pre></td><td >False</td></tr><tr ><td >ProbabilityDistribution</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
Distribution = Output[Reports][ScoringDistribution][Slider][data],
ConvertedTable = Table.FromRows(Distribution, {"Confidence Threshold Tmp", "TP", "FP", "TN", "FN"}),
#"Changed Type" = Table.TransformColumnTypes(ConvertedTable, {{"Confidence Threshold Tmp", type number}, {"TP", Int64.Type}, {"FP", Int64.Type}, {"TN", Int64.Type}, {"FN", Int64.Type}}),
BUGFIX1 = Table.AddColumn(#"Changed Type", "Confidence Threshold", each [Confidence Threshold Tmp]+.01),
BUGFIX2 = Table.TransformColumnTypes(BUGFIX1,{{"Confidence Threshold", type number}}),
BUGFIX3 = Table.RemoveColumns(BUGFIX2,{"Confidence Threshold Tmp"}),
BUGFIX4 = Table.InsertRows(BUGFIX3, 0, {[TP=List.Max(BUGFIX3[TP]), FP=List.Max(BUGFIX3[FP]), TN=0, FN=0, Confidence Threshold=0.00]}),
#"Added Custom" = Table.AddColumn(BUGFIX4, "Population", each ([TP]+[FP])/([TP]+[FP]+[TN]+[FN])),
#"Changed Type1" = Table.TransformColumnTypes(#"Added Custom",{{"Population", Percentage.Type}, {"Confidence Threshold", type number}})
in
#"Changed Type1"</pre></td><td >False</td></tr><tr ><td >LabelForTrueOutcomes</td><td ></td><td >Import</td><td ><pre>"1" meta [IsParameterQuery=true, Type="Text", IsParameterQueryRequired=true]</pre></td><td >False</td></tr><tr ><td >LabelForFalseOutcomes</td><td ></td><td >Import</td><td ><pre>"Not 1" meta [IsParameterQuery=true, Type="Text", IsParameterQueryRequired=true]</pre></td><td >False</td></tr><tr ><td >Parameters</td><td ></td><td >Import</td><td ><pre>let
paramlist = {{"WorkspaceId",WorkspaceId}, {"DataflowId",DataflowId}, {"EntityName",EntityName}, {"TrueLabel",LabelForTrueOutcomes}, {"FalseLabel", LabelForFalseOutcomes}, {"LabelColumnName", LabelColumnName}, {"LastBookmarkState", LastBookmarkState}, {"LastProbabilityThreshold", LastProbabilityThreshold}},
#"Converted to Table" = Table.FromList(paramlist , Splitter.SplitByNothing(), null, null, ExtraValues.Error),
#"Extracted Values" = Table.TransformColumns(#"Converted to Table", {"Column1", each Text.Combine(List.Transform(_, Text.From), ","), type text}),
#"Split Column by Delimiter" = Table.SplitColumn(#"Extracted Values", "Column1", Splitter.SplitTextByDelimiter(",", QuoteStyle.Csv), {"Column1.1", "Column1.2"}),
#"Changed Type" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"Column1.1", type text}, {"Column1.2", type text}}),
#"Renamed Columns" = Table.RenameColumns(#"Changed Type",{{"Column1.1", "ParameterName"}, {"Column1.2", "ParameterValue"}})
in
#"Renamed Columns"</pre></td><td >False</td></tr><tr ><td >WorkspaceId</td><td ></td><td >Import</td><td ><pre>"87fdfdfd-8174-4499-94cd-8d0dec621a6e" meta [IsParameterQuery=true, Type="Text", IsParameterQueryRequired=true]</pre></td><td >False</td></tr><tr ><td >DataflowId</td><td ></td><td >Import</td><td ><pre>"50d115fd-a0b4-43d2-9afa-ba2e09b9f29e" meta [IsParameterQuery=true, Type="Text", IsParameterQueryRequired=true]</pre></td><td >False</td></tr><tr ><td >EntityName</td><td ></td><td >Import</td><td ><pre>"David ML" meta [IsParameterQuery=true, Type="Text", IsParameterQueryRequired=true]</pre></td><td >False</td></tr><tr ><td >LabelColumnName</td><td ></td><td >Import</td><td ><pre>"Buyer" meta [IsParameterQuery=true, Type="Text", IsParameterQueryRequired=true]</pre></td><td >False</td></tr><tr ><td >Featurizer</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
featurizer = Output[Featurizers],
ConvertedTable = Table.FromRows(featurizer, {"Column1", "Column2"}),
#"Changed Type" = Table.TransformColumnTypes(ConvertedTable, {{"Column1", type text}, {"Column2", type text}}),
#"Split Column by Delimiter" = Table.SplitColumn(#"Changed Type", "Column2", Splitter.SplitTextByEachDelimiter({","}, QuoteStyle.Csv, false), {"Column2.1", "Column2.2"}),
#"Changed Type1" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"Column2.1", type text}, {"Column2.2", type text}}),
#"Split Column by Delimiter1" = Table.SplitColumn(#"Changed Type1", "Column2.2", Splitter.SplitTextByEachDelimiter({","}, QuoteStyle.Csv, false), {"Column2.2.1", "Column2.2.2"}),
#"Changed Type2" = Table.TransformColumnTypes(#"Split Column by Delimiter1",{{"Column2.2.1", type text}, {"Column2.2.2", type text}}),
#"Split Column by Delimiter2" = Table.SplitColumn(#"Changed Type2", "Column2.1", Splitter.SplitTextByDelimiter(":", QuoteStyle.Csv), {"Column2.1.1", "Column2.1.2"}),
#"Changed Type3" = Table.TransformColumnTypes(#"Split Column by Delimiter2",{{"Column2.1.1", type text}, {"Column2.1.2", type text}}),
#"Split Column by Delimiter3" = Table.SplitColumn(#"Changed Type3", "Column2.2.1", Splitter.SplitTextByDelimiter(":", QuoteStyle.Csv), {"Column2.2.1.1", "Column2.2.1.2"}),
#"Changed Type4" = Table.TransformColumnTypes(#"Split Column by Delimiter3",{{"Column2.2.1.1", type text}, {"Column2.2.1.2", type text}}),
#"Split Column by Delimiter4" = Table.SplitColumn(#"Changed Type4", "Column2.2.2", Splitter.SplitTextByDelimiter(":", QuoteStyle.Csv), {"Column2.2.2.1", "Column2.2.2.2"}),
#"Changed Type5" = Table.TransformColumnTypes(#"Split Column by Delimiter4",{{"Column2.2.2.1", type text}, {"Column2.2.2.2", type text}}),
#"Renamed Columns" = Table.RenameColumns(#"Changed Type5",{{"Column1", "Feature"},{"Column2.1.2", "Detected Column Type"}, {"Column2.2.1.2", "Imputation"}, {"Column2.2.2.2", "Transformations"}}),
#"Removed Columns" = Table.RemoveColumns(#"Renamed Columns",{"Column2.1.1", "Column2.2.1.1", "Column2.2.2.1"}),
#"Added Conditional Column" = Table.AddColumn(#"Removed Columns", "Details", each if Text.Contains([Detected Column Type], "Categorical") then "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#data-pre-processing-and-featurization" else if Text.Contains([Detected Column Type], "Numeric") then "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#data-pre-processing-and-featurization" else if Text.Contains([Detected Column Type], "CategoricalHash") then "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#data-pre-processing-and-featurization" else if Text.Contains([Detected Column Type], "Text") then "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#data-pre-processing-and-featurization" else if Text.Contains([Detected Column Type], "DateTime") then "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#data-pre-processing-and-featurization" else "https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train")
in
#"Added Conditional Column"</pre></td><td >False</td></tr><tr ><td >ProbabilityDistributionUnpivot</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
Distribution = Output[Reports][ScoringDistribution][Slider][data],
ConvertedTable = Table.FromRows(Distribution, {"Confidence Threshold", "True Positive", "False Positive", "True Negative", "False Negative"}),
#"Changed Type" = Table.TransformColumnTypes(ConvertedTable, {{"Confidence Threshold", type number}, {"True Positive", Int64.Type}, {"False Positive", Int64.Type}, {"True Negative", Int64.Type}, {"False Negative", Int64.Type}}),
#"Unpivoted Columns" = Table.UnpivotOtherColumns(#"Changed Type", {"Confidence Threshold"}, "Attribute", "Value"),
#"Added Conditional Column" = Table.AddColumn(#"Unpivoted Columns", "Actual", each if [Attribute] = "True Positive" then "Actual Win" else if [Attribute] = "False Negative" then "Actual Win" else if [Attribute] = "True Negative" then "Actual Loss" else if [Attribute] = "False Positive" then "Actual Loss" else null),
#"Renamed Columns1" = Table.RenameColumns(#"Added Conditional Column",{{"Attribute", "Predicted"}})
in
#"Renamed Columns1"</pre></td><td >False</td></tr><tr ><td >ROC</td><td ></td><td >Import</td><td ><pre>let
Source = Table.NestedJoin(FPR,{"Index"},TPR,{"Index"},"TPR",JoinKind.Inner),
#"Expanded TPR" = Table.ExpandTableColumn(Source, "TPR", {"TPR"}, {"TPR.1"}),
#"Removed Columns" = Table.RemoveColumns(#"Expanded TPR",{"Index"}),
#"Renamed Columns" = Table.RenameColumns(#"Removed Columns",{{"TPR.1", "TPR"}}),
#"Changed Type" = Table.TransformColumnTypes(#"Renamed Columns",{{"TPR", type number}, {"FPR", type number}})
in
#"Changed Type"</pre></td><td >False</td></tr><tr ><td >IterationDetails</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
TrainingIterationsNode = Source[TrainingIterations],
#"Converted to Table" = Table.FromList(TrainingIterationsNode, Splitter.SplitByNothing(), null, null, ExtraValues.Error),
#"Expanded Column1" = Table.ExpandRecordColumn(#"Converted to Table", "Column1", {"pipeline", "score"}, {"Column1.pipeline", "Column1.score"}),
#"Extracted Values1" = Table.TransformColumns(#"Expanded Column1", {"Column1.pipeline", each Text.Combine(List.Transform(_, Text.From), ";"), type text}),
#"Split Column by Delimiter" = Table.SplitColumn(#"Extracted Values1", "Column1.pipeline", Splitter.SplitTextByEachDelimiter({";"}, QuoteStyle.Csv, false), {"Column1.pipeline.1", "Column1.pipeline.2"}),
#"Changed Type" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"Column1.pipeline.1", type text}, {"Column1.pipeline.2", type text}}),
#"Added Custom" = Table.AddColumn(#"Changed Type", "Scaler", each [Column1.pipeline.1] & ")"),
#"Removed Columns" = Table.RemoveColumns(#"Added Custom",{"Column1.pipeline.1"}),
#"Renamed Columns" = Table.RenameColumns(#"Removed Columns",{{"Column1.score", "Score"}, {"Column1.pipeline.2", "Estimator"}}),
#"Added Index" = Table.AddIndexColumn(#"Renamed Columns", "Index", 1, 1),
#"Reordered Columns" = Table.ReorderColumns(#"Added Index",{"Index", "Estimator", "Score", "Scaler"}),
#"Changed Type1" = Table.TransformColumnTypes(#"Reordered Columns",{{"Score", Percentage.Type}}),
#"Inserted Text Before Delimiter" = Table.AddColumn(#"Changed Type1", "Text Before Delimiter", each Text.BeforeDelimiter([Estimator], "("), type text),
#"Renamed Columns1" = Table.RenameColumns(#"Inserted Text Before Delimiter",{{"Text Before Delimiter", "Estimator Name Temp"}}),
#"Added Conditional Column" = Table.AddColumn(#"Renamed Columns1", "Estimator Name", each if Text.Contains([Estimator], "ExtraTreesClassifier") then "Extra Trees Classifier" else if Text.Contains([Estimator], "LightGBMClassifier") then "Light GBM Classifier" else if Text.Contains([Estimator], "RandomForestClassifier") then "Random Forest Classifier" else if Text.Contains([Estimator], "LogisticRegression") then "Logistic Regression" else if Text.Contains([Estimator], "GradientBoostingClassifier") then "Gradient Boosting Classifier" else if Text.Contains([Estimator], "PreFittedSoftVotingClassifier") then "Pre-fitted Soft Voting Classifier" else [Estimator Name Temp]),
#"Removed Columns1" = Table.RemoveColumns(#"Added Conditional Column",{"Estimator Name Temp"})
in
#"Removed Columns1"</pre></td><td >False</td></tr><tr ><td >IterationDetailsMax</td><td ></td><td >Import</td><td ><pre>let
Source = IterationDetails,
MaxScoreRow = Table.Max(Source, "Score"),
ParameterRowValue = Record.Field(MaxScoreRow, "Estimator"),
ParameterValues = Text.BetweenDelimiters(ParameterRowValue, "(", ")"),
ParameterValuesSplit = Text.Split(ParameterValues, ","),
TableConverted = Table.FromList(ParameterValuesSplit),
#"Split Column by Delimiter" = Table.SplitColumn(TableConverted, "Column1", Splitter.SplitTextByEachDelimiter({"="}, QuoteStyle.Csv, false), {"Column1.1", "Column1.2"}),
#"Renamed Columns" = Table.RenameColumns(#"Split Column by Delimiter",{{"Column1.1", "Parameter Name"}, {"Column1.2", "Parameter Value"}})
in
#"Renamed Columns"</pre></td><td >False</td></tr><tr ><td >EnsembleIterationFullDetails</td><td ></td><td >Import</td><td ><pre>let
Source = Table.Join(EnsembleWeights, {"Index"}, EnsembleIterations, {"Index"}),
#"Merged Queries" = Table.NestedJoin(Source,{"Iteration"},EnsembleIterationAlgorithmNames,{"Index"},"EnsembleIterationAlgorithmNames",JoinKind.Inner),
#"Expanded EnsembleIterationAlgorithmNames" = Table.ExpandTableColumn(#"Merged Queries", "EnsembleIterationAlgorithmNames", {"Index", "Details"}, {"EnsembleIterationAlgorithmNames.Index", "EnsembleIterationAlgorithmNames.Details"}),
#"Expanded EnsembleIterationAlgorithmNames.Details" = Table.ExpandRecordColumn(#"Expanded EnsembleIterationAlgorithmNames", "EnsembleIterationAlgorithmNames.Details", {"pipeline", "score"}, {"EnsembleIterationAlgorithmNames.Details.pipeline", "EnsembleIterationAlgorithmNames.Details.score"}),
#"Extracted Values" = Table.TransformColumns(#"Expanded EnsembleIterationAlgorithmNames.Details", {"EnsembleIterationAlgorithmNames.Details.pipeline", each Text.Combine(List.Transform(_, Text.From), "#(tab)"), type text}),
#"Split Column by Delimiter" = Table.SplitColumn(#"Extracted Values", "EnsembleIterationAlgorithmNames.Details.pipeline", Splitter.SplitTextByDelimiter("#(tab)", QuoteStyle.Csv), {"EnsembleIterationAlgorithmNames.Details.pipeline.1", "EnsembleIterationAlgorithmNames.Details.pipeline.2"}),
#"Changed Type" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"EnsembleIterationAlgorithmNames.Details.pipeline.1", type text}, {"EnsembleIterationAlgorithmNames.Details.pipeline.2", type text}, {"Weights", Percentage.Type}, {"Iteration", Int64.Type}, {"Index", Int64.Type}, {"EnsembleIterationAlgorithmNames.Index", Int64.Type}, {"EnsembleIterationAlgorithmNames.Details.score", Percentage.Type}}),
#"Inserted Text Before Delimiter" = Table.AddColumn(#"Changed Type", "Text Before Delimiter", each Text.BeforeDelimiter([EnsembleIterationAlgorithmNames.Details.pipeline.2], "("), type text),
#"Renamed Columns" = Table.RenameColumns(#"Inserted Text Before Delimiter",{{"Text Before Delimiter", "Algorithm Name"}, {"EnsembleIterationAlgorithmNames.Details.score", "Algorithm Score"}, {"EnsembleIterationAlgorithmNames.Details.pipeline.2", "Algorithm Details"}}),
#"Duplicated Column" = Table.DuplicateColumn(#"Renamed Columns", "Algorithm Details", "Algorithm Details - Copy"),
#"Extracted Text Between Delimiters" = Table.TransformColumns(#"Duplicated Column", {{"Algorithm Details - Copy", each Text.BetweenDelimiters(_, "(", ")"), type text}}),
#"Renamed Columns1" = Table.RenameColumns(#"Extracted Text Between Delimiters",{{"Algorithm Details - Copy", "Algorithm Parameters"}}),
#"Split Column by Delimiter1" = Table.ExpandListColumn(Table.TransformColumns(#"Renamed Columns1", {{"Algorithm Parameters", Splitter.SplitTextByDelimiter(",", QuoteStyle.None), let itemType = (type nullable text) meta [Serialized.Text = true] in type {itemType}}}), "Algorithm Parameters"),
#"Changed Type1" = Table.TransformColumnTypes(#"Split Column by Delimiter1",{{"Algorithm Parameters", type text}}),
#"Split Column by Delimiter2" = Table.SplitColumn(#"Changed Type1", "Algorithm Parameters", Splitter.SplitTextByDelimiter("=", QuoteStyle.Csv), {"Algorithm Parameters.1", "Algorithm Parameters.2"}),
#"Changed Type2" = Table.TransformColumnTypes(#"Split Column by Delimiter2",{{"Algorithm Parameters.1", type text}, {"Algorithm Parameters.2", type text}}),
#"Renamed Columns2" = Table.RenameColumns(#"Changed Type2",{{"Algorithm Parameters.1", "Algorithm Parameter Name"}, {"Algorithm Parameters.2", "Algorithm Parameter Value"}})
in
#"Renamed Columns2"</pre></td><td >False</td></tr><tr ><td >IsEnsemble</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
EnsembleDetailsRecord = Source[EnsembleDetails],
#"IsEnsemble" =
if EnsembleDetailsRecord <> null then
true
else
false
in
#"IsEnsemble"</pre></td><td >False</td></tr><tr ><td >Selector Table</td><td ></td><td >Import</td><td ><pre>VALUES('SliderValue'[Confidence Threshold])</pre></td><td >False</td></tr><tr ><td >PopulationSize</td><td ></td><td >Import</td><td ><pre>GENERATESERIES(0, 100000, 1)</pre></td><td >False</td></tr><tr ><td >UnitBenefit</td><td ></td><td >Import</td><td ><pre>GENERATESERIES(0, 100000, 1)</pre></td><td >False</td></tr><tr ><td >UnitCost</td><td ></td><td >Import</td><td ><pre>GENERATESERIES(0, 100000, 1)</pre></td><td >False</td></tr><tr ><td >KeyInfluencers_TopPredictors</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
KeyInfluencers1 = Output[KeyInfluencers],
TopPredictors1 = Table.FromRows(KeyInfluencers1[TopPredictors], {"Feature Name", "Feature Type", "Feature Importance"}),
#"Changed Type" = Table.TransformColumnTypes(TopPredictors1,{{"Feature Importance", type number}})
in
#"Changed Type"</pre></td><td >False</td></tr><tr ><td >KeyInfluencers_Breakdowns</td><td ></td><td >Import</td><td ><pre>let
Source = Output,
KeyInfluencers1 = Output[KeyInfluencers],
PredictorBreakdowns1 = Table.FromRows(KeyInfluencers1[PredictorBreakdowns], {"Feature Name", "Bin", "% of Positive Outcome"}),
#"Changed Type" = Table.TransformColumnTypes(PredictorBreakdowns1,{{"% of Positive Outcome", type number}}),
#"Filtered Rows" = Table.SelectRows(#"Changed Type", each [#"% of Positive Outcome"] <> 0)
in
#"Filtered Rows"</pre></td><td >False</td></tr></table><hr></hr><br></br><h3><div>List of Measures:</div></h3><br></br><table border="1" cellpadding="3">
<tr>
<th>Measure Name</th>
<th>Table Name</th>
<th>Description</th>
<th>Expression</th>
<th>Dependency</th>
<th>Reverse Dependency</th>
</tr>
<tr>
<td>10KScenarioStatement</td>
<td>Output</td>
<td></td>
<td><pre>"If applied to 10,000 records with the same distribution of " & Parameters[TrueLabel] & " and " & Parameters[FalseLabel] & " records as your input data, this model will yield the below predictions."</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KScenarioStatement</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: FalseLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KScenarioStatement</span><span class="dropDownCaret">
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>10KPrecisionStatement</td>
<td>Output</td>
<td></td>
<td><pre>"This model will predict " & FORMAT([10KPredictedWins], "0") & " records as " & Parameters[TrueLabel] & " records, of which " & FORMAT([10KTruePositiveWins], "0") & " will be actual " & Parameters[TrueLabel]& " records."</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KPrecisionStatement</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: 10KPredictedWins (Table: Output)</span>
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></li>
<li><span class="caret">Measure: 10KTruePositiveWins (Table: Output)</span>
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KPrecisionStatement</span><span class="dropDownCaret">
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>10KRecallStatement</td>
<td>Output</td>
<td></td>
<td><pre>"There will be " & FORMAT([10KActualWins], "0") & " actual " & Parameters[TrueLabel] & " records in your data, of which this model will correctly predict " & FORMAT([10KTruePositiveWins], "0") & " as " & Parameters[TrueLabel] & " records."</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KRecallStatement</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: 10KActualWins (Table: Output)</span>
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></li>
<li><span class="caret">Measure: 10KTruePositiveWins (Table: Output)</span>
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KRecallStatement</span><span class="dropDownCaret">
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>10KActualWins</td>
<td>Output</td>
<td></td>
<td><pre>
VAR selectedValue = MAX(SliderValue[Confidence Threshold])
VAR TrueP = CALCULATE(AVERAGE(ProbabilityDistribution[TP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR TrueN = CALCULATE(AVERAGE(ProbabilityDistribution[TN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseP = CALCULATE(AVERAGE(ProbabilityDistribution[FP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseN = CALCULATE(AVERAGE(ProbabilityDistribution[FN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
return (TrueP + FalseN) * 10000 / (TrueP + FalseN + TrueN + FalseP)</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KActualWins</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KActualWins</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Measure: 10KRecallStatement</span>
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>10KPredictedWins</td>
<td>Output</td>
<td></td>
<td><pre>
VAR selectedValue = MAX(SliderValue[Confidence Threshold])
VAR TrueP = CALCULATE(AVERAGE(ProbabilityDistribution[TP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR TrueN = CALCULATE(AVERAGE(ProbabilityDistribution[TN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseP = CALCULATE(AVERAGE(ProbabilityDistribution[FP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseN = CALCULATE(AVERAGE(ProbabilityDistribution[FN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
return (TrueP + FalseP) * 10000 / (TrueP + FalseN + TrueN + FalseP)</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KPredictedWins</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KPredictedWins</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Measure: 10KPrecisionStatement</span>
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>10KTruePositiveWins</td>
<td>Output</td>
<td></td>
<td><pre>
VAR selectedValue = MAX(SliderValue[Confidence Threshold])
VAR TrueP = CALCULATE(AVERAGE(ProbabilityDistribution[TP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR TrueN = CALCULATE(AVERAGE(ProbabilityDistribution[TN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseP = CALCULATE(AVERAGE(ProbabilityDistribution[FP]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
VAR FalseN = CALCULATE(AVERAGE(ProbabilityDistribution[FN]), ProbabilityDistribution[Confidence Threshold] = selectedValue)
return (TrueP) * 10000 / (TrueP + FalseN + TrueN + FalseP)</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KTruePositiveWins</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: 10KTruePositiveWins</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Measure: 10KPrecisionStatement</span>
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></li>
<li><span class="caret">Measure: 10KRecallStatement</span>
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>PrecisionSubtext</td>
<td>Output</td>
<td></td>
<td><pre>"of records predicted as " & Parameters[TrueLabel] & " are likely to actually be " & Parameters[TrueLabel] </pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: PrecisionSubtext</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: PrecisionSubtext</td>
</tr>
<tr>
<td>RecallSubtext</td>
<td>Output</td>
<td></td>
<td><pre>"of records that are actually " & Parameters[TrueLabel] & " are likely to be predicted as " & Parameters[TrueLabel]</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: RecallSubtext</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: RecallSubtext</td>
</tr>
<tr>
<td>PositivePredictorHeading</td>
<td>Output</td>
<td></td>
<td><pre>"Top predictors by influence" </pre></td>
<td>Measure: PositivePredictorHeading</td>
<td>Measure: PositivePredictorHeading</td>
</tr>
<tr>
<td>PositivePredictorBreakdownHeader</td>
<td>Output</td>
<td></td>
<td><pre>IF(SELECTEDVALUE(PositivePredictor[Predictor], FALSE()) = FALSE(), "[Click on a predictor to see outcome breakdown]",
SELECTEDVALUE(PositivePredictor[Predictor], BLANK()) & " breakdown")</pre></td>
<td>Measure: PositivePredictorBreakdownHeader</td>
<td>Measure: PositivePredictorBreakdownHeader</td>
</tr>
<tr>
<td>Area under ROC curve</td>
<td>Output</td>
<td></td>
<td><pre>CALCULATE(SELECTEDVALUE(Output[Value]), Output[Name] = "TrainingAUC") * 1</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Area under ROC curve</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></span></li>
</ul></td>
<td>Measure: Area under ROC curve</td>
</tr>
<tr>
<td>Input row count</td>
<td>Output</td>
<td></td>
<td><pre>CALCULATE(SELECTEDVALUE(Output[Value]), Output[Name] = "TotalDataSize") * 1</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Input row count</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></span></li>
</ul></td>
<td>Measure: Input row count</td>
</tr>
<tr>
<td>Sampled row count</td>
<td>Output</td>
<td></td>
<td><pre>Output[Training row count] + Output[Validation row count] </pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Sampled row count</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li><span class="caret">Measure: Training row count (Table: Output)</span>
<ul class="nested">
<li>Table: Output ...</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></li>
<li><span class="caret">Measure: Validation row count (Table: Output)</span>
<ul class="nested">
<li>Table: Output ...</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: Sampled row count</td>
</tr>
<tr>
<td>Training row count</td>
<td>Output</td>
<td></td>
<td><pre>CALCULATE(SELECTEDVALUE(Output[Value]), Output[Name] = "TrainingSize") * 1 </pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Training row count</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Training row count</span><span class="dropDownCaret">
<ul class="nested">
<li>Measure: Sampled row count</li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>Validation row count</td>
<td>Output</td>
<td></td>
<td><pre>CALCULATE(SELECTEDVALUE(Output[Value]), Output[Name] = "TestingSize") * 1 </pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Validation row count</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Validation row count</span><span class="dropDownCaret">
<ul class="nested">
<li>Measure: Sampled row count</li>
<li>Measure: ModelTested</li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>ModelTested</td>
<td>Output</td>
<td></td>
<td><pre>"The model predicted "& Parameters[LabelColumnName] & " probabilities for a test set of " & Output[Validation row count] & " records and compared the predicted outcomes (based on the selected threshold) to the historical outcomes." </pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: ModelTested</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li>Table: Parameters</li>
<li><span class="caret">Measure: Validation row count (Table: Output)</span>
<ul class="nested">
<li>Table: Output ...</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></li>
<li><span class="caret">Measure: LabelColumnName (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: ModelTested</td>
</tr>
<tr>
<td>TrainingSubtitleThreshold</td>
<td>Output</td>
<td></td>
<td><pre>
VAR selectedValue = MAX(SliderValue[Confidence Threshold])
return "At your selected confidence threshold of " & selectedValue</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: TrainingSubtitleThreshold</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
</ul></span></li>
</ul></td>
<td>Measure: TrainingSubtitleThreshold</td>
</tr>
<tr>
<td>Benefit Analysis Title</td>
<td>Output</td>
<td></td>
<td><pre>Parameters[TrueLabel] & " in your data"</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Benefit Analysis Title</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: Benefit Analysis Title</td>
</tr>
<tr>
<td>Cost Analysis Title</td>
<td>Output</td>
<td></td>
<td><pre>Parameters[FalseLabel] & " in your data"</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Cost Analysis Title</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: FalseLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: Cost Analysis Title</td>
</tr>
<tr>
<td>Numbe Of Iterations</td>
<td>Output</td>
<td></td>
<td><pre>CALCULATE(SELECTEDVALUE(Output[Value]), Output[Name] = "NumberOfIterations") * 1</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Numbe Of Iterations</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></span></li>
</ul></td>
<td>Measure: Numbe Of Iterations</td>
</tr>
<tr>
<td>Pipeline Steps</td>
<td>Output</td>
<td></td>
<td><pre>CALCULATE(SELECTEDVALUE(Output[Value]), Output[Name] = "PipelineStepsString")</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Pipeline Steps</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Output</li>
<li>Column: Name (Table: Output)</li>
<li>Column: Value (Table: Output)</li>
</ul></span></li>
</ul></td>
<td>Measure: Pipeline Steps</td>
</tr>
<tr>
<td>Model Accuracy</td>
<td>Output</td>
<td></td>
<td><pre>"The likely accuracy of predictions generated by your machine learning model can be interpreted using a Cumulative Gains chart and the Receiver Operating Characteristics (ROC) curve." </pre></td>
<td>Measure: Model Accuracy</td>
<td>Measure: Model Accuracy</td>
</tr>
<tr>
<td>ModelTraining</td>
<td>Output</td>
<td></td>
<td><pre>"Power BI used the automated ML capability in Azure Machine Learning to train your model. Automated ML was used to find the best way to prepare your data, determine the algorithms used and select the algorithm parameters likely to yield the best accuracy. These steps were used in the machine learning pipeline which generated your machine learning model."</pre></td>
<td>Measure: ModelTraining</td>
<td>Measure: ModelTraining</td>
</tr>
<tr>
<td>Final Model Quality</td>
<td>Output</td>
<td></td>
<td><pre>MAX(IterationDetails[Score])</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Final Model Quality</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: IterationDetails</li>
<li>Column: Score (Table: IterationDetails)</li>
</ul></span></li>
</ul></td>
<td>Measure: Final Model Quality</td>
</tr>
<tr>
<td>ModelParametersTitle</td>
<td>Output</td>
<td></td>
<td><pre>[Algorithm Selected] & " final parameters selected"</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: ModelParametersTitle</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Measure: Algorithm Selected (Table: IterationDetails)</span>
<ul class="nested">
<li>Table: IterationDetails ...</li>
<li>Column: Score (Table: IterationDetails)</li>
<li>Column: Estimator Name (Table: IterationDetails)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: ModelParametersTitle</td>
</tr>
<tr>
<td>InterpretResultsDynamicText</td>
<td>Output</td>
<td></td>
<td><pre>"As a result of the comparison, we have 4 bins: True Positives, False Positives, True Negatives and False Negatives." & UNICHAR(10) & UNICHAR(10) & "True Positives and True Negatives are correctly predicted " & Parameters[TrueLabel] & " and " & Parameters[FalseLabel] & " events, respectively. False Positives are observations where a " & Parameters[FalseLabel] & " was predicted as a " & Parameters[TrueLabel] & ". Conversely, False Negatives are observations where a " & Parameters[TrueLabel] & " was predicted as a " & Parameters[FalseLabel] & "." & UNICHAR(10) & UNICHAR(10) & " Note, the probability threshold used to classify a prediction as a " & Parameters[TrueLabel] & " or a " & Parameters[FalseLabel] & " affects these values." </pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: InterpretResultsDynamicText</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: FalseLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: InterpretResultsDynamicText</td>
</tr>
<tr>
<td>DecideApplyDynamicText</td>
<td>Output</td>
<td></td>
<td><pre>"The model scores every record with a probability of " & Parameters[TrueLabel] & " ranging from 0 to 1. You can decide the threshold above which an observation is predicted as a "& Parameters[TrueLabel] & ". Increasing the threshold will result in fewer false positives (higher precision). Decreasing it will result in fewer false negatives (higher recall)." & UNICHAR(10) & UNICHAR(10) &
" High recall causes the model to be liberal when labelling outcomes as " & Parameters[TrueLabel] & " ,to ensure that you don’t miss any. This means some " & Parameters[FalseLabel] & " will slip through the cracks into this category. Whereas, high precision causes the model to label outcomes as " & Parameters[TrueLabel] & " when confidence is high, to minimize mislabeling. This means some " & Parameters[TrueLabel] & " will be missed."</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: DecideApplyDynamicText</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: FalseLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: DecideApplyDynamicText</td>
</tr>
<tr>
<td>MatrixPredictedTrue</td>
<td>Output</td>
<td></td>
<td><pre>"Predicted " & Parameters[TrueLabel]</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: MatrixPredictedTrue</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: MatrixPredictedTrue</td>
</tr>
<tr>
<td>MatrixPredictedFalse</td>
<td>Output</td>
<td></td>
<td><pre>"Predicted " & Parameters[FalseLabel]</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: MatrixPredictedFalse</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: FalseLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: MatrixPredictedFalse</td>
</tr>
<tr>
<td>MatrixActualTrue</td>
<td>Output</td>
<td></td>
<td><pre>"Actual " & Parameters[TrueLabel]</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: MatrixActualTrue</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: MatrixActualTrue</td>
</tr>
<tr>
<td>MatrixActualFalse</td>
<td>Output</td>
<td></td>
<td><pre>"Actual " & Parameters[FalseLabel]</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: MatrixActualFalse</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: FalseLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td>Measure: MatrixActualFalse</td>
</tr>
<tr>
<td>Precision recall text</td>
<td>Output</td>
<td></td>
<td><pre>"Given all records that were predicted as " & Parameters[TrueLabel] & ", precision refers to how many of those records were correctly predicted. On the other hand, out of all records that are actually " & Parameters[TrueLabel] & ", recall refers to how many of them were correctly predicted."</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Precision recall text</span><span class="dropDownCaret">
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></span></li>
</ul></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Precision recall text</span><span class="dropDownCaret">
<ul class="nested">
<li>Measure: Precision and recall</li>
</ul></span></li>
</ul></td>
</tr>
<tr>
<td>Precision and recall</td>
<td>Output</td>
<td></td>
<td><pre>[Precision recall text] & UNICHAR(10) & UNICHAR(10) & [10KScenarioStatement] & UNICHAR(10) & UNICHAR(10) & [10KPrecisionStatement] & UNICHAR(10) & UNICHAR(10) & [10KRecallStatement]</pre></td>
<td><ul id="myUL">
<li><span class="caret">Measure: Precision and recall</span><span class="dropDownCaret">
<ul class="nested">
<li><span class="caret">Measure: 10KScenarioStatement (Table: Output)</span>
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: FalseLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
<li><span class="caret">Measure: TrueLabel (Table: Parameters)</span>
<ul class="nested">
<li>Table: Parameters ...</li>
<li>Column: ParameterName (Table: Parameters)</li>
<li>Column: ParameterValue (Table: Parameters)</li>
</ul></li>
</ul></li>
<li><span class="caret">Measure: 10KPrecisionStatement (Table: Output)</span>
<ul class="nested">
<li>Table: Parameters</li>
<li><span class="caret">Measure: 10KPredictedWins (Table: Output)</span>
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></li>
<li><span class="caret">Measure: 10KTruePositiveWins (Table: Output)</span>
<ul class="nested">
<li><span class="caret">Calc_Table: SliderValue (Table: SliderValue)</span>
<ul class="nested">
<li>Table: ProbabilityDistribution</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
</ul></li>
<li>Table: ProbabilityDistribution</li>
<li>Calc_Column: Confidence Threshold (Table: SliderValue)</li>
<li>Column: Confidence Threshold (Table: ProbabilityDistribution)</li>
<li>Column: TP (Table: ProbabilityDistribution)</li>
<li>Column: FP (Table: ProbabilityDistribution)</li>
<li>Column: TN (Table: ProbabilityDistribution)</li>
<li>Column: FN (Table: ProbabilityDistribution)</li>
</ul></li>