Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

CodeSignal

Ensembles in Machine Learning

via CodeSignal

Overview

Learn about ensemble learning techniques, such as bagging, boosting, and stacking, which combine multiple models to achieve superior predictive performance.

Syllabus

  • Lesson 1: Bagging in Machine Learning
    • Adjust the Number of Estimators
    • Train and Evaluate Bagging Classifier
    • Optimize Bagging Classifier for Wine Classification
    • Enhance Your Bagging Classifier
  • Lesson 2: Random Forest in Machine Learning
    • Adjusting Random Forest Tree Depth
    • Complete the Random Forest Classifier for Wine Dataset
    • Improving Random Forest for Wine Classification
    • Evaluate Random Forest Accuracy with Varying Depths
  • Lesson 3: Boosting with AdaBoost in Machine Learning
    • Change the Weak Classifier in AdaBoost
    • Train and Predict with AdaBoost
    • AdaBoost vs RandomForest
  • Lesson 4: Gradient Boosting in Machine Learning
    • Adjust Gradient Boosting Estimators
    • Complete the Gradient Boosting Setup for Digit Classification
    • Gradient Boosting vs. AdaBoost on Synthetic Data
    • Comparing Models Efficiency
    • Gradient Boosting with Varying Estimators
  • Lesson 5: Stacking in Machine Learning
    • Change Meta-Model to Gradient Boosting
    • Change the Meta-Model in Stacking Classifier
    • Complete the Stacking Classifier
    • Tune the Stacking Classifier

Reviews

Start your review of Ensembles in Machine Learning

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.