When building a decision tree model, yoeu will often want to limit the tree size so that building the time buidling the model is reduced or to prevent over fitting. In this article, we will learn to limit the size of a random tree model using, Sklearn.
To limit the size of a tree, we have several options. The main option
max_depth depth. Setting this paramter will limit how deep the tree branches its decisions.
from sklearn.tree import DecisionTreeClassifier from sklearn import datasets iris = datasets.load_iris() features = iris.data target = iris.target decisiontree = DecisionTreeClassifier(max_depth = 5, min_samples_split = 2, min_samples_leaf = 1, min_weight_fraction_leaf = 0, max_leaf_nodes = None, min_impurity_decrease = 0) model = decisiontree.fit(features, target) print(model.score())