Learn to implement classification trees in Python from start to finish in this comprehensive webinar. Explore the entire process, including importing modules and data, handling missing data, formatting data with one-hot encoding, building preliminary trees, pruning techniques, and creating the final tree. Gain practical insights into visualizing alpha values and applying cross-validation for optimal tree construction. Perfect for those already familiar with decision trees, cross-validation, confusion matrices, cost complexity pruning, and concepts of bias, variance, and overfitting.
Overview
Syllabus
This webinar was recorded 20200528 at am New York time.
Awesome song and introduction
Import Modules
Import Data
Missing Data Part 1: Identifying
Missing Data Part 2: Dealing with it
Format Data Part 1: X and y
Format Data Part 2: One-Hot Encoding
Build Preliminary Tree
Pruning Part 1: Visualize Alpha
Pruning Part 2: Cross Validation
Build and Draw Final Tree
Taught by
StatQuest with Josh Starmer
