What you'll learn

Decision trees are one of the most widely used machine learning models due to their simplicity, interpretability, and effectiveness in both classification and regression tasks. However, building a high-performing decision tree model requires a solid understanding of its structure, optimization techniques, and evaluation metrics.

In this course, Advanced Decision Trees, you’ll learn to build, evaluate, and fine-tune decision trees for better accuracy and interpretability. First, you’ll explore the different types of decision trees and their applications, understanding when to use them. Next, you’ll discover how to evaluate decision tree models using key performance metrics like accuracy and RMSE, while also analyzing feature importance. Finally, you’ll learn how to optimize decision tree models by tuning hyperparameters, applying pruning techniques, and preventing overfitting. When you’re finished with this course, you’ll have the skills and knowledge to effectively build and refine decision tree models, ensuring they perform optimally in real-world scenarios.