InTDS ArchivebyVinícius TrevisanUsing SHAP Values to Explain How Your Machine Learning Model WorksLearn to use a tool that shows how each feature affects every prediction of the modelJan 17, 20227Jan 17, 20227
InTDS ArchivebyVassily MorozovUnlocking Insights: Building a Scorecard with Logistic RegressionAfter a credit card? An insurance policy? Ever wondered about the three-digit number that shapes these decisions?Feb 15, 2024Feb 15, 2024
InDataman in AIbyChris Kuo/Dr. DatamanExplain Your Model with the SHAP ValuesUse the SHAP Values to Explain Any Complex ML ModelSep 14, 201938Sep 14, 201938
InTDS ArchivebyLina FaikHow can Machine Learning algorithms include better Causality?In recent years, machine learning algorithms have known a great success. Thanks to the availability of important amount of data and the…Apr 22, 20202Apr 22, 20202
InTDS ArchivebyDipanjan (DJ) SarkarHands-on Machine Learning Model InterpretationA comprehensive guide to interpreting machine learning modelsDec 13, 201823Dec 13, 201823
InTDS ArchivebyMoto DEIThree Model Explanability Methods Every Data Scientist Should KnowPermutation importance and partial dependence plot new version of scikit-learn 0.22 supports (celebration🎉!) and SHAP as a bonus.Dec 18, 20193Dec 18, 20193
InTDS ArchivebyScott LundbergBe Careful When Interpreting Predictive Models in Search of Causal InsightsA careful exploration of the pitfalls of trying to extract causal insights from modern predictive machine learning models.May 17, 202122May 17, 202122