Trees are one of the most powerful machine learning models you can use. They break down functions into break points and decision trees that can be interpreted much easier than deep learning models. They also have great performance. In this article, we will learn how to build a Tree Classifier in Sklearn.
To create a tree model, we use the
DecisionTreeClassifier class. We use this similar to any other model; we create an instance, then pass our x and y data to the
from sklearn.tree import DecisionTreeClassifier from sklearn import datasets iris = datasets.load_iris() features = iris.data target = iris.target decisiontree = DecisionTreeClassifier() model = decisiontree.fit(features, target) model.fit()