Machine Learning with Tree-Based Models in Python

About This Course

This course offers a comprehensive examination of decision trees, which are supervised learning models employed for both classification and regression tasks. Tree models are highly adaptable, enabling them to effectively capture complex non-linear relationships; however, they may also risk overfitting by memorizing noise in the dataset. Ensemble methods, which consolidate predictions from multiple trained trees, leverage this adaptability while minimizing the potential for overfitting. These techniques are widely utilized across various domains and are recognized for their success in machine learning competitions. In this course, you will learn how to use Python and the user-friendly scikit-learn library to train decision trees and tree-based models. You will analyze the benefits and drawbacks of tree models, practice ensemble techniques to mitigate these issues using real-world datasets, and gain expertise in fine-tuning key hyperparameters to improve model performance.