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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

Getting Started with Gradient Boosting Algorithms

Prerequisites Python and ML concepts

Level:

Intermediate

Boosting Mechanics
Explain the sequential nature of boosting algorithms and how they differ from other ensemble methods.
Gradient Boosting Implementation
Implement Gradient Boosting Machines (GBM) for regression and classification using the Scikit-Learn library.
Advanced Libraries
Utilize modern, optimized gradient boosting libraries including XGBoost, LightGBM, and CatBoost to train models efficiently.
Hyperparameter Optimization
Apply structured techniques like Grid Search and Randomized Search to tune model hyperparameters for improved performance.
Model Interpretation
Analyze feature importance and use partial dependence plots to understand model predictions.

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