A Gentle Introduction to Ensemble Learning

A Gentle Introduction to Ensemble Learning

In this post, you will discover a gentle introduction to ensemble learning.

Many decisions we make in life are based on the opinions of multiple other people.

This includes choosing a book to read based on reviews, choosing a course of action based on the advice of multiple medical doctors, and determining guilt.

Often, decision making by a group of individuals results in a better outcome than a decision made by any one member of the group. This is generally referred to as the wisdom of the crowd.

We can achieve a similar result by combining the predictions of multiple machine learning models for regression and classification predictive modeling problems. This is referred to generally as ensemble machine learning, or simply ensemble learning.

In this post, you will discover a gentle introduction to ensemble learning.

After reading this post, you will know:

  • Many decisions we make involve the opinions or votes of other people.
  • The ability of groups of people to make better decisions than individuals is called the wisdom of the crowd.
  • Ensemble machine learning involves combining predictions from multiple skillful models.

Let’s get started.

Overview

This tutorial is divided into three parts; they are:

  1. Making Important Decisions
  2. Wisdom of Crowds
  3. Ensemble Machine Learning

Making Important Decisions

Consider important decisions you make in your life.

For example:

  • What book to purchase and read next.
  • What university to attend.

Candidate books are those that sound interesting, but the book we purchase might have the most favorable reviews. Candidate universities are those that offer the courses we’re interested in, but we might choose one based on the feedback from friends and acquaintances that have first-hand experience.

We might trust the reviews and star ratings because each individual that contributed a review was (hopefully) unaffiliated with the book and independent of the other people leaving a review. When this is not the case, trust in the outcome is questionable and trust in the system is shaken, which is why Amazon works hard to delete fake reviews for books.

Also, consider important decisions we make more personally.

For example, medical treatment for an illness.

We take advice from an expert, but we seek a second, third, and even more opinions to confirm we are taking the best course of action.

The advice from the second and third opinion may or may not match the first opinion, but we weigh it heavily because it is provided dispassionately, objectively, and independently. If the doctors colluded on their opinion, then we would feel like the process of seeking a second and third opinion has failed.

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