Published Apr 2, 2024

771: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko

Discover the power of gradient boosting with Kirill Eremenko as he joins Jon Krohn to unravel advanced techniques like XGBoost, LightGBM, and CatBoost, highlighting their transformative effects on model accuracy and machine learning competition.

Episode Highlights

Topics covered

Episode Highlights

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771: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko

Topics covered

Popular Clips

LightGBM Overview

Categorical Variable Encoding

Gradient Boosting Explained

Chaining Decision Trees

Categorical Boosting Explained

Bootstrapping Explained

Target Leakage Solutions

Target Encoding Explained

Efficient Model Optimization

Boosting Techniques Explained

Random Forests & Boosting

Gradient Boosting Explained

Decision Trees Explained

Live Labs Promotion

Random Forest Insights

Episode Highlights

Gradient Boosting

Advanced Boosting Techniques

Decision Trees

Related Episodes

681: XGBoost: The Ultimate Classifier — with Matt Harrison

694: CatBoost: Powerful, efficient ML for large tabular datasets — with Jon Krohn (@JonKrohnLearns)

649: Introduction to Machine Learning — with Kirill Eremenko and Hadelin de Ponteves

747: Technical Intro to Transformers and LLMs — with Kirill Eremenko

671: Cloud Machine Learning — with Kirill Eremenko and Hadelin de Ponteves

721: Quantum Machine Learning — with Dr. Amira Abbas

759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko

661: Designing Machine Learning Systems — with Chip Huyen

786: The Six Keys to Data Scientists' Success — with Kirill Eremenko

853: Generative AI for Business — with Kirill Eremenko and Hadelin de Ponteves

SDS 435: Scaling Up Machine Learning — with Erica Greene

793: Bayesian Methods and Applications — with Alexandre Andorra

SDS 599: MLOps: Machine Learning Operations — with @Miki_ML

627: AutoML: Automated Machine Learning — with Erin LeDell

737: scikit-learn's Past, Present and Future — with scikit-learn co-founder Dr. Gaël Varoquaux

771: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko

Topics covered

Popular Clips

Episode Highlights

Gradient BoostingKirill Eremenko joins Jon Krohn to explore the intricacies of gradient boosting, a technique that significantly enhances model accuracy. They discuss its core principles, advantages over other methods, and practical applications in business settings.

Gradient Boosting

Advanced Boosting TechniquesKirill Eremenko explores the leading gradient-boosting algorithms: XGBoost, LightGBM, and CatBoost. He highlights their unique features and applications, emphasizing their impact on competitive machine learning.

Advanced Boosting Techniques

Decision TreesKirill Eremenko delves into the intricacies of decision trees, highlighting their role in both regression and classification tasks. He further explores how these trees form the foundation for powerful ensemble methods like random forests and gradient boosting.

Decision Trees

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