Advance Machine Learning | 6. Ensemble & Boosting Methods by Abraham | Learn Smarter
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6. Ensemble & Boosting Methods

Ensemble methods, including Bagging, Boosting, and Stacking, enhance predictive accuracy by combining multiple model predictions. Boosting techniques such as AdaBoost, Gradient Boosting, and XGBoost highlight sequential learning that corrects previous errors, while newer methods like LightGBM and CatBoost improve efficiency and adaptability. These approaches are pivotal in machine learning applications, particularly in scenarios requiring high accuracy.

Sections

  • 6

    Ensemble & Boosting Methods

    Ensemble methods improve machine learning model performance by combining predictions from multiple models.

    Learning Practice

  • 6.1

    What Are Ensemble Methods?

    Ensemble methods enhance model performance by combining predictions from multiple base learners to achieve greater accuracy and robustness.

    Learning Practice

  • 6.2

    Bagging (Bootstrap Aggregating)

    Bagging is an ensemble method that improves model stability by creating multiple subsets of training data using bootstrapping.

    Learning Practice

  • 6.3

    Boosting Overview

    Boosting is a sequential ensemble method that focuses on improving model accuracy by correcting errors of previous models.

    Learning Practice

  • 6.4

    Adaboost (Adaptive Boosting)

    AdaBoost is a boosting technique that enhances the performance of weak learners by adjusting the weights of training samples based on classification errors.

    Learning Practice

  • 6.5

    Gradient Boosting Machines (Gbm)

    Gradient Boosting Machines (GBM) focus on sequentially improving model predictions by correcting errors through optimization techniques.

    Learning Practice

  • 6.6

    Xgboost (Extreme Gradient Boosting)

    XGBoost is a powerful, scalable, and regularized version of gradient boosting, designed for speed and performance.

    Learning Practice

  • 6.7

    Lightgbm And Catboost

    LightGBM and CatBoost are advanced boosting techniques designed to improve model training efficiency and handle categorical data effectively.

    Learning Practice

  • 6.8

    Stacking (Stacked Generalization)

    Stacking combines predictions from multiple models into one final prediction with the help of a meta-model, enhancing performance through model diversity.

    Learning Practice

  • 6.9

    Practical Applications And Use Cases

    This section highlights the practical applications of ensemble methods and their significance in various domains.

    Learning Practice

  • 6.10

    Advantages And Limitations

    This section outlines the advantages and limitations associated with ensemble methods in machine learning.

    Learning Practice

References

AML ch6.pdf

Class Notes

Memorization

What we have learnt

  • Ensemble methods combine mu...
  • Bagging reduces variance by...
  • Boosting focuses on correct...

Final Test

Revision Tests