Module 4: Advanced Supervised Learning & Evaluation

This section explores advanced supervised learning techniques, particularly ensemble methods like Bagging and Boosting, to enhance model accuracy and robustness.

Week 7: Ensemble Methods

This section explores ensemble methods in supervised learning, focusing on techniques that combine multiple models to improve predictive performance.

Ensemble Learning Concepts

Ensemble learning combines predictions from multiple models to improve performance and robustness over single models.

Bagging (Bootstrap Aggregating)

Bagging is an ensemble method that reduces variance by training multiple models on different subsets of data and aggregating their predictions.

Boosting

Boosting is an ensemble learning technique that sequentially trains models to improve prediction accuracy by focusing on the errors of previous models.

Bagging: Random Forest

This section explores the Random Forest algorithm, a powerful ensemble method based on bagging, which improves model accuracy and robustness by combining multiple decision trees.

Principles Of Random Forest

Random Forest is a powerful ensemble learning method that enhances prediction accuracy through the aggregation of multiple decision trees.

Advantages Of Random Forest

This section details the numerous advantages of the Random Forest algorithm in machine learning, particularly in enhancing predictive accuracy and reducing overfitting.

Feature Importance (Understanding What Matters To The Model)

This section discusses how Random Forest quantifies feature importance, providing insight into which features significantly influence the model's predictions.

Boosting

Boosting is a powerful ensemble method that improves model accuracy by training weak learners sequentially, where each learner focuses on correcting the errors of its predecessor.

Adaboost (Adaptive Boosting)

AdaBoost is an early and powerful boosting algorithm that focuses on improving model accuracy by sequentially adjusting weights of misclassified examples using simple models called weak learners.

Gradient Boosting Machines (Gbm)

Gradient Boosting Machines (GBMs) represent a robust and versatile ensemble technique that sequentially builds models to reduce prediction errors.

Xgboost, Lightgbm, Catboost (Modern Boosting Powerhouses)

This section introduces XGBoost, LightGBM, and CatBoost as advanced, optimized boosting algorithms that enhance traditional Gradient Boosting Machine techniques.

Lab: Implementing And Comparing Various Ensemble Methods, Focusing On Their Performance Improvements

This lab provides practical experience in implementing and comparing various ensemble methods, focusing on their performance improvements over single machine learning models.

Prepare A Suitable Dataset For Ensemble Learning

This section outlines the steps for preparing datasets specifically designed for effective use of ensemble learning techniques in supervised machine learning.

Implement A Base Learner For Baseline Comparison

This section discusses how to implement a single decision tree as a baseline learner to compare against ensemble methods for better performance evaluation.

Implement Bagging: Random Forest

This section delves into Bagging, particularly focusing on the Random Forest algorithm, illustrating its principles, advantages, and applications in machine learning.

Implement Boosting: Gradient Boosting Machines (Gbm)

Gradient Boosting Machines (GBM) are a powerful ensemble technique that sequentially builds models to correct errors made by previous predictions.

Implement Modern Boosting Algorithms (Xgboost, Lightgbm, Catboost)

This section introduces modern boosting algorithms such as XGBoost, LightGBM, and CatBoost, highlighting their features and optimizations that make them popular in machine learning competitions.

Perform Comprehensive Performance Comparison And Analysis

This section provides insights into various ensemble methods in machine learning, focusing on performance comparisons and analyses of multiple models.

Discussion And Reflection On Ensemble Learning

This section delves into ensemble learning methods, discussing how various models can enhance predictive performance through techniques like bagging and boosting.