Machine Learning with Tree-Based Models in R

Description

In this course, you will  :

• Discover how to make decision trees, split data, and predict which patients are most likely to have diabetes.
• You'll overcome statistical insecurities associated with single train/test splits by using cool techniques like cross-validation, and then you'll go even deeper by mastering the bias-variance tradeoff.
• Learn how to use boosted trees, a powerful machine learning technique that builds high-performing predictive models using ensemble learning.
• Learn about their fine-tuning and how to compare different models to determine which one will be produced.

Syllabus :

• Classification Trees
• Regression Trees and Cross-Validation
• Hyperparameters and Ensemble Models
• Boosted Trees