Machine Learning with Tree-Based Models in Python

Description

In this course, you will learn :

• Regression and classification Trees (CART) are a class of supervised learning models used to solve classification and regression problems.
• The CART algorithm was introduced.
• Understand how to diagnose overfitting and underfitting issues.
• Introduced to the concept of ensembling, which is the process by which the predictions of several models are aggregated to produce more robust predictions.
• comprehend how bagging can be used to construct a tree ensemble
• Learn how the random forests algorithm can increase ensemble diversity by randomising each split in the trees that make up the ensemble.

Syllabus :

• Classification and Regression Trees
• The Bias-Variance Tradeoff
• Bagging and Random Forests
• Boosting
• Model Tuning