Decision Trees (CART) algorithm

Decision Trees (CART) algorithm

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Linear regression and logistic regression can't display collaborations between highlights. The Classification And Regression Trees (CART) algorithm is the most straightforward and famous tree algorithm, and models a basic collaboration between highlights

To fabricate the tree, we pick each time the element that parts our information the most ideal way that is available. How would we measure the qualitiy of a split ? We apply rules, for example, the cross-entropy or Gini pollutant. We stop the improvement of the tree while parting a hub doesn't bring down the debasement

To implement decision trees in Python, we can use scikit-learn:

clf = DecisionTreeClassifier(max_depth=3), y_train) y_pred = clf.predict(X_test) accuracy_score(y_pred, y_test) 0.9210526315789473

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