CART explained: Simplifying classification and regression trees in Machine Learning

Join Jangwon Park, a PhD student from the University of Toronto and a Statistics Without Borders volunteer, in part 2 of our Machine Learning lesson series. In this video, we explore CART (Classification and Regression Trees), a pivotal model in machine learning used for both classification and regression tasks.

What You'll Learn:

An overview of CART and its applications in healthcare systems.
Key machine learning concepts like overfitting, hyperparameter tuning, and cross-validation.
A detailed walkthrough of how CART works, including feature splitting and node classification.
Insights into variable importance and how different features influence predictions.

Hands-On Example: We'll use the Pima Indians diabetes dataset, which includes various medical features to classify whether a patient is diabetic or not. You'll see how to conduct exploratory data analysis (EDA), fit a CART model using R, and evaluate the model's performance through visualizations and accuracy metrics.