I visualized my decisiontreeclassifier and I noticed, that the sum of samples are wrong or formulated differently the 'value' value does not fit with the value of the samples(Screenshot)? Do I misinterpret my Decisiontree? I thought if got 100 samples in my node and 40 are True and 60 are False, I got in my next node 40 (or 60) samples which are divided again... import matplotlib.pyplot as plt from sklearn import tree tree1=DecisionTreeClassifier(criterion="entropy",max_features=13,max_leaf_nodes=75,min_impurity_decrease=0.001,min_samples_leaf=12,min_samples_split=20,splitter="best",max_depth=9) tree1.fit(X_train,y_train) feature_names=Daten.drop("Abwanderung_LabelEncode",axis=1).columns class_names=["Keine Abwanderung","Abwanderung"] fig = plt.figure(figsize=(25,20)) _ = tree.plot_tree(tree1, feature_names=feature_names, class_names=class_names, rounded=True, filled=True) Continue reading...
