Decision Tree in Python Using scikit-learn: The Complete Guide with Code

Author(s): Davide Nardini

Originally published on Towards AI.

In this article, I’ll guide you through your first training session on a Machine Learning Algorithm: we’ll be training a Decision Tree in Python using scikit-learn.

Photo by Johann Siemens on Unsplash

The Decision Tree stands as one of the most famous and fundamental Machine Learning Algorithms. It serves as the foundation for more sophisticated models like Random Forest, Gradient Boosting, and XGBoost.

Throughout this article, I’ll walk you through training a Decision Tree in Python using scikit-learn on the Iris Species Dataset, known as the “Hello World” of Machine Learning Classification tasks.

What is a Decision Tree and How it WorksAnnotation: Supervised Learning and Classification/RegressionTraining a Decision Tree using scikit-learn (sk-learn)Evaluation of the performances of the Decision TreeImprove the model performances: Hyperparameter OptimizationFinal Thoughts

Before we delve into training our Machine Learning model, let’s introduce what a Decision Tree is and how it functions.

The Decision Tree serves as a supervised machine-learning algorithm that proves valuable for both classification and regression tasks.

Understanding the terms “decision” and “tree” is pivotal in grasping this algorithm: essentially, the decision tree makes decisions by analyzing data and constructing a tree-like structure to facilitate this process.

Think of it as a sophisticated “if-then-else” construct, primarily with binary responses. We pose sequential… Read the full blog for free on Medium.

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Published via Towards AI