Advanced Modeling Decision Trees with CART

About

Learn the intuition and mechanics of decision trees and build, tune, evaluate, and visualize effective models. (CART = Classification and Regression Trees)

Learning Objectives

You will be able to:

Understand the intuition behind decision trees.
Understand the CART algorithm that powers decision trees.
Describe how decision trees use Gini to make decisions.
Fit, generate predictions from, and evaluate decision tree models.
Tune max_depth, min_samples_split, min_samples_leaf to get the best possible tree.
Visualize a decision tree.

Content

Topic	Skills
Slides	- Understand the intuition behind decision trees
Decision Trees	- Prepare data for using CART - Implement Decision Trees with CART using `scikit-learn`

Prerequisites

Write and run Python code in a Jupyter notebook.
Create basic Python functions.
Have familiarity with pandas.
Have familiarity with scikit-learn.
Understand what supervised learning is, and how to run linear regression and a logistic regression and / or knn classification model.

References

📖 Reference Materials

Important note

Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)