ensembling

date	duration	maintainer	order	title
w6d2	75	robert	10	Ensembling

Sample Lesson Plan

• (75m) Ensembling

Learning Objectives

Students can

• Understand and explain key concepts of ensembling models.
  • Different methods
    • Averaging, Bagging, and other sampling based methods.
    • Boosting
    • Stacking
  • How ensembling affects over/underfitting.
• Apply Random Forest methods to a supervised project.
  • Explain and tune hyperparameters.
  • Respond to over or underfitting.
• Use meta-estimators to apply ensembling to other models.

Depends On

Classification and Regression Trees

Ensemble model uses: Linear Model, Naive Bayes, KNN, SVC, Random Forest, Extra Trees, AdaBoost

Instructor Notes

Additional Resources

• Estimate of unique samples in bootstrap
• Bagging and Boosting - MIT
• Stacking Classifier