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All other instances are assigned to node 3 or node 4, depending on whether values of feature x2 exceed 1. Instances with a value greater than 3 for feature x1 end up in node 5. I recommend the book ‘The Elements of Statistical Learning’ (Friedman, Hastie and Tibshirani 2009) 18 for a more detailed introduction to CART.įIGURE 5.16: Decision tree with artificial data. We will focus on CART, but the interpretation is similar for most other tree types. The classification and regression trees (CART) algorithm is probably the most popular algorithm for tree induction. They differ in the possible structure of the tree (e.g. number of splits per node), the criteria how to find the splits, when to stop splitting and how to estimate the simple models within the leaf nodes. There are various algorithms that can grow a tree. Trees can be used for classification and regression. To predict the outcome in each leaf node, the average outcome of the training data in this node is used. The final subsets are called terminal or leaf nodes and the intermediate subsets are called internal nodes or split nodes. Through splitting, different subsets of the dataset are created, with each instance belonging to one subset. Tree based models split the data multiple times according to certain cutoff values in the features. Linear regression and logistic regression models fail in situations where the relationship between features and outcome is nonlinear or where features interact with each other. 10.5.4 Disadvantages of Identifying Influential Instances.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).
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9.3.1 Generating Counterfactual Explanations.9.1 Individual Conditional Expectation (ICE).
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