Decision Trees are very flexible, easy to understand, and easy to debug. They will work with classification problems and regression problems. So if you are trying to predict a categorical value like (red, green, up, down) or if you are trying to predict a continuous value like 2.9, 3.4 etc Decision Trees will handle both … Read more
I would agree with @Falcon w.r.t. the dataset size. It’s likely that the main problem is the small size of the dataset. If possible, the best thing you can do is get more data, the more data (generally) the less likely it is to overfit, as random patterns that appear predictive start to get drowned … Read more
Some quick preliminaries: Let’s say we have a classification problem with K classes. In a region of feature space represented by the node of a decision tree, recall that the “impurity” of the region is measured by quantifying the inhomogeneity, using the probability of the class in that region. Normally, we estimate: Pr(Class=k) = #(examples … Read more
(This is just a reformat of my comment above from 2016…it still holds true.) The accepted answer for this question is misleading. As it stands, sklearn decision trees do not handle categorical data – see issue #5442. The recommended approach of using Label Encoding converts to integers which the DecisionTreeClassifier() will treat as numeric. If … Read more
I believe that this answer is more correct than the other answers here: from sklearn.tree import _tree def tree_to_code(tree, feature_names): tree_ = tree.tree_ feature_name = [ feature_names[i] if i != _tree.TREE_UNDEFINED else “undefined!” for i in tree_.feature ] print “def tree({}):”.format(“, “.join(feature_names)) def recurse(node, depth): indent = ” ” * depth if tree_.feature[node] != _tree.TREE_UNDEFINED: … Read more