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

Ensemble Methods

Go to class Write review

Overview

Learn about Ensemble Methods and their implementation from scratch in C++ using mlpack. This course covers the understanding and implementation of multiple ensemble methods such as Bagging, Random Forest, AdaBoost, and Stacking. Focus on building these models and aggregating their results without relying on high-level machine learning libraries beyond mlpack.

Syllabus

Unit 1: Bagging with Decision Trees

Iris Flower Species Prediction Using Bagging Technique
Bootstrapping Function for Bagging Algorithm
Bagging Predictions Implementation Task
Decision Tree Prediction Aggregation Task

Unit 2: Random Forests in C++

Assessing Random Forest Classifier Accuracy on Iris Dataset
Adjusting Random Forest Max Depth for Optimal Accuracy
Implementing a Random Forest Constructor in C++

Unit 3: AdaBoost in C++

Efficient Spam Email Filtering with AdaBoost Classifier
Experimenting with AdaBoost Learning Rate
Updating Weights in AdaBoost Model
Complete the AdaBoost Prediction Function

Unit 4: Stacking Ensemble Learning

Iris Species Prediction Using Stacking Model
Switching Meta-Model to Decision Tree Classifier
Stacking Model Predictions Preparation

Reviews

Start your review of Ensemble Methods