Feature Engineering & Interpretability for XGBoost with Board Game Ratings

Explore advanced modeling techniques using #TidyTuesday data on board game ratings in this 48-minute screencast. Dive into custom feature engineering, xgboost tuning, and explainability methods. Learn about data overview, average distribution, preprocessing, custom tokenizing, and string squishing. Delve into regression, tuning, and result analysis. Discover how to plot function games, find the best game, and work with testing sets. Gain insights into explainability tools, including parsnip fit, model importance, and dependency partial plots. Examine min age plots and create summaries to enhance your understanding of feature engineering and interpretability in xgboost models.

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