Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Feature Engineering & Interpretability for XGBoost with Board Game Ratings

Julia Silge via YouTube

Overview

Google, IBM & Meta Certificates – 40% Off
One Coursera Plus subscription covers most Professional Certificates on Coursera.
Unlock All Certificates
This advanced screencast demonstrates custom feature engineering, hyperparameter tuning, and model explainability for an XGBoost regression model predicting board-game ratings. It uses R and tidy modeling tools with SHAP-based feature-importance and partial-dependence plots.

Syllabus

Introduction
Data overview
Average distribution
Modeling
Preprocessing
Custom Tokenizing
String Squish
Regression
Tuning
Results
Plotting function game
Finding the best game
Last fit
Testing set
Explainability tools
parsnip fit
model importance
shop
model
other arguments
matrix
making plots
dependency partial plot
min age plot
summary

Taught by

Julia Silge

Reviews

Start your review of Feature Engineering & Interpretability for XGBoost with Board Game Ratings

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.