Super Data Science: ML & AI Podcast with Jon Krohn 771: Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko
Apr 2, 2024
Machine learning expert Kirill Eremenko discusses decision trees, random forests, and the top gradient boosting algorithms: XGBoost, LightGBM, and CatBoost. Topics include advantages of XGBoost, LightGBM efficiency, and CatBoost's handling of categorical variables.
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Ensemble Methods
- Ensemble methods combine multiple models, often weak learners like decision trees.
- They are effective because they capture nonlinear relationships and are quick to train.
Decision Trees
- Decision trees use if-else splits based on variables like income or age to make predictions.
- A new customer's path through the tree determines the predicted outcome, like spending on candles.
Jelly Bean Analogy
- Kirill Eremenko uses the analogy of guessing jelly beans in a jar at a fair to explain random forests.
- Averaging individual guesses, like a random forest averages predictions, provides the best guess.