![]() 10.5.3 Advantages of Identifying Influential Instances.10.3.5 Bonus: Other Concept-based Approaches.10.3.1 TCAV: Testing with Concept Activation Vectors.10.2.1 Vanilla Gradient (Saliency Maps).9.6 SHAP (SHapley Additive exPlanations).9.3.1 Generating Counterfactual Explanations.9.1 Individual Conditional Expectation (ICE).8.5.2 Should I Compute Importance on Training or Test Data?.8.4.5 Generalized Functional ANOVA for Dependent Features.8.4.3 How not to Compute the Components II.8.4.1 How not to Compute the Components I.8.2 Accumulated Local Effects (ALE) Plot.5.5.1 Learn Rules from a Single Feature (OneR).5.2.1 What is Wrong with Linear Regression for Classification?.5.1.6 Do Linear Models Create Good Explanations?.4.3 Risk Factors for Cervical Cancer (Classification).4.2 YouTube Spam Comments (Text Classification).3.3.5 Local Interpretability for a Group of Predictions. ![]() 3.3.4 Local Interpretability for a Single Prediction. ![]()
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