Decision Trees

Discover Decision Trees, an intuitive and interpretable machine learning model for both classification and regression. Learn how Decision Trees split data based on features to make predictions and are commonly used in areas like customer segmentation, healthcare, and more. Implement Decision Trees in Python using libraries like scikit-learn.

Introduction

Introduction to Decision Trees, a versatile algorithm used for classification and regression tasks.

Theory

Delve into the theoretical foundations of Decision Trees, covering key concepts like tree structure, Gini Impurity, Information Gain, pruning, and the trade-offs between tree depth and model generalization.

scikit-learn Example

Train, prune, evaluate, and inspect a multiclass decision tree without leaking test data.

TensorFlow Guidance

Choose a maintained decision-tree implementation alongside TensorFlow without confusing forests with neural-network training.

PyTorch Guidance

Understand why classical decision-tree fitting belongs in a tree library rather than a hand-built PyTorch recursion.

Common Mistakes & Best Practices

Discover common pitfalls in Decision Trees and learn best practices for optimization.

Comparison with Other Algorithms

Compare Decision Trees with other algorithms to understand where they shine and where they don't.