All Courses

Mastering Gradient Boosting Algorithms

Chapter 1: Gradient Boosting Foundations Revisited

Ensemble Methods: A Recap

Decision Trees as Base Learners

The Additive Modeling Framework

Gradient Descent Fundamentals

Introducing the Gradient Boosting Machine (GBM)

Chapter 2: The Gradient Boosting Algorithm in Depth

Functional Gradient Descent

Deriving the Generic GBM Algorithm

Common Loss Functions for Regression

Common Loss Functions for Classification

The Role of Shrinkage (Learning Rate)

Subsampling Techniques (Stochastic Gradient Boosting)

Implementing GBM with Scikit-learn

Practice: Building a Basic GBM Model

Chapter 3: Regularization in Gradient Boosting

Overfitting Challenges in Boosting

Tree Constraints: Depth, Nodes, and Splits

Shrinkage as Implicit Regularization

Subsampling (Stochastic Gradient Boosting)

Regularized Objective Functions (L1/L2)

Early Stopping Strategies

Hands-on Practical: Applying Regularization

Chapter 4: XGBoost: Extreme Gradient Boosting

Motivation and Enhancements over GBM

The Regularized Learning Objective

Split Finding Algorithm: Exact Greedy

Split Finding Algorithm: Approximate Greedy

Sparsity-Aware Split Finding

System Optimizations: Cache Awareness and Parallelism

XGBoost API: Parameters and Configuration

Hands-on Practical: Implementing XGBoost

Chapter 5: LightGBM: Light Gradient Boosting Machine

Motivation: Addressing XGBoost's Limitations

Gradient-based One-Side Sampling (GOSS)

Exclusive Feature Bundling (EFB)

Histogram-Based Split Finding

Leaf-Wise Tree Growth

Optimized Categorical Feature Handling

LightGBM API: Parameters and Configuration

Hands-on Practical: Implementing LightGBM

Chapter 6: CatBoost: Gradient Boosting on Decision Trees

Motivation: Challenges with Categorical Data

Ordered Target Statistics (Ordered TS)

Addressing Prediction Shift: Ordered Boosting

Handling Feature Combinations

Oblivious Trees

GPU Training Acceleration

CatBoost API: Parameters and Configuration

Hands-on Practical: Implementing CatBoost

Chapter 7: Advanced Topics and Customization

Model Interpretability with SHAP

TreeSHAP for Gradient Boosting Models

Global vs. Local Explanations

Probability Calibration for Classification

Implementing Custom Loss Functions

Implementing Custom Evaluation Metrics

Handling Imbalanced Datasets with Boosting

Practice: Custom Objectives and SHAP

Chapter 8: Hyperparameter Optimization Strategies

The Importance of Hyperparameter Tuning

Identifying Critical Hyperparameters

Systematic Tuning: Grid Search and Randomized Search

Advanced Tuning: Bayesian Optimization

Hyperparameter Optimization Frameworks (Optuna, Hyperopt)

Tuning Strategy: From Coarse to Fine

Cross-Validation Strategies for Tuning

Hands-on Practical: Advanced Tuning with Optuna

Chapter 9: Gradient Boosting for Specialized Tasks

Learning to Rank with Gradient Boosting

Ranking Objective Functions (Pairwise, Listwise)

Survival Analysis with Gradient Boosting

Survival Objective Functions (Cox PH)

Quantile Regression with Gradient Boosting

Quantile Loss Function Implementation

Multi-Output Gradient Boosting

Practice: Implementing Ranking with XGBoost

Tree Constraints: Depth, Nodes, and Splits

Was this section helpful?

References

Classification and Regression Trees, Leo Breiman, Jerome Friedman, Richard A. Olshen, and Charles Stone, 1984 (Chapman and Hall/CRC) DOI: 10.1201/9781315139470 - A seminal text on decision tree algorithms, detailing methods for splitting, pruning, and complexity control of trees.
Greedy Function Approximation: A Gradient Boosting Machine, Jerome H. Friedman, 2001 The Annals of Statistics, Vol. 29 (Institute of Mathematical Statistics) DOI: 10.1214/aos/1013203451 - Introduces the gradient boosting machine algorithm, explaining its iterative ensemble construction with weak learners.
XGBoost: A Scalable Tree Boosting System, Tianqi Chen and Carlos Guestrin, 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/2939672.2939785 - Describes the XGBoost algorithm, covering regularization parameters like gamma and min_child_weight for tree complexity.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A comprehensive textbook covering decision trees, ensemble methods, and regularization techniques in statistical learning.
Parameters - LightGBM Documentation, Microsoft and LightGBM Contributors, 2024 - Official documentation detailing hyperparameters for LightGBM, including max_depth and num_leaves, and their impact on tree complexity.

© 2025 ApX Machine LearningEngineered with