Getting Started with Gradient Boosting Algorithms
Chapter 1: Foundations of Ensemble Learning and Boosting
What are Ensemble Methods?
Bagging vs. Boosting
Introduction to the Boosting Principle
The AdaBoost Algorithm: A Precursor to Gradient Boosting
Understanding Weak Learners
Bias-Variance Tradeoff in Ensembles
Chapter 2: The Gradient Boosting Machine (GBM)
From Boosting to Gradient Boosting
The Role of Gradients and Residuals
The GBM Algorithm Step-by-Step
Loss Functions for Regression Tasks
Loss Functions for Classification Tasks
Hands-on Practical: Building a GBM with Python
Chapter 3: Implementing Gradient Boosting with Scikit-Learn
Scikit-Learn's GradientBoostingClassifier
Scikit-Learn's GradientBoostingRegressor
Fitting and Predicting with GBM Models
Interpreting Model Parameters
Feature Importance in GBM
Partial Dependence Plots for Model Interpretation
Hands-on Practical: Building a Predictive Model
Chapter 4: Advanced Gradient Boosting: XGBoost
Why XGBoost? Speed and Performance
Architectural Improvements over Standard GBM
Regularization in XGBoost (L1 and L2)
Handling Missing Values Automatically
Installing and Setting up XGBoost
The XGBoost API: A Walkthrough
Hands-on Practical: Training an XGBoost Model
Chapter 5: Advanced Gradient Boosting: LightGBM and CatBoost
Introduction to LightGBM: Gradient-based One-Side Sampling
LightGBM's Exclusive Feature Bundling
Introduction to CatBoost: Handling Categorical Features
CatBoost's Ordered Boosting and Symmetric Trees
Performance Comparison: XGBoost vs. LightGBM vs. CatBoost
Hands-on Practical: Implementing LightGBM and CatBoost
Chapter 6: Hyperparameter Tuning and Model Optimization
The Importance of Hyperparameter Tuning
Principal Hyperparameters in Gradient Boosting
Tuning the Number of Estimators and Learning Rate
Controlling Tree Complexity
Subsampling Parameters for Regularization
A Structured Approach to Tuning
Using Grid Search and Randomized Search
Hands-on Practical: Optimizing a Gradient Boosting Model
Fitting and Predicting with GBM Models
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- sklearn.ensemble.GradientBoostingRegressor, scikit-learn developers, 2024 (scikit-learn project) - Official documentation describing the parameters and methods (.fit(), .predict()) of the GradientBoostingRegressor class, which is central to the section's practical examples.
- Greedy Function Approximation: A Gradient Boosting Machine, Jerome H. Friedman, 2001 The Annals of Statistics, Vol. 29 (Institute of Mathematical Statistics (IMS)) DOI: 10.1214/aos/1013203451 - This seminal paper introduces the Gradient Boosting Machine algorithm, explaining its theoretical foundations and mechanism for sequential error correction, which forms the basis of the discussed models.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A foundational textbook on statistical learning, offering in-depth coverage of ensemble methods, including the theoretical background and practical aspects of gradient boosting.