Tag - Bagging

Data Science Basics: An Introduction to Ensemble Learners

New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers. By Matthew Mayo, KDnuggets. Algorithm selection can be...

Ensemble Learning to Improve Machine Learning Results

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). By Vadim Smolyakov, Statsbot. Ensemble learni...

Simulating the bias-variance tradeoff in R

Share Tweet In my last blog post, I have elaborated on the Bagging algorithm and showed its prediction performance via simulation. Here, I want to go into the details on how to simulate the bias and variance of a nonparametric regression fitting meth...

What is the difference between Bagging and Boosting?

Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Here we explain in detail. comments By xristica, Quantdare. Bagging and Boosting are similar in that they are both ensemble techniques, where a set of...