Ensemble Methods: Bagging vs Boosting

Ensemble Methods

Ensemble methods are techniques that create multiple models and then combine them to produce improved results. Ensemble methods usually produce more accurate solutions than a single model would.

The main causes of error in learning are due to noise, bias, and variance. Ensemble helps to minimize these factors. These methods are designed to…

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Machine Learning Models and Security

Creating a Custom TFX Executor

TFX pipeline

Naive Bayes Classifier

Simulation of 2-p Microscopy Imaging Acquisition

Essential Linear Algebra for Machine Learning (ML)

What cooperative game theory says about the Israeli parliament

Teams formed from our algorithm. It shows three politicians whose voting pattern deviate from party lines

MACHINE LEARNING (ML) ON GOOGLE CLOUD

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Jagandeep Singh

Jagandeep Singh

More from Medium

Experiments with: Altair

Chessmapper: Let’s Talk About That KPIs

Prototyping Data Science Features

Intuition of the Delta Method in Statistics