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