ML Concept Ensemble Learning I — Bagging & Random Forest

<blockquote> This article aims to examine the fundamental concepts of Ensemble Learning, a subset of machine learning methods that have gained widespread use worldwide. We will also introduce the common types of Ensemble Learning. However, due to the diverse nature of Ensemble Learning, we will break down the entire concept into multiple parts to provide a detailed introduction. In this section, we will focus on presenting Bagging and the most commonly used Bagging algorithm: Random Forest. </blockquote>   <h1>Outline</h1> The article is divided into three main parts: <ol> <li>Prior Knowledge</li> <li>Bagging(Bootstrap Aggregating)</li> <li>Algorithms</li> </ol> Supplement (OOB proof) References Initially, we will introduce the concept of Ensemble Learning. Following that, we will delve into how Bagging works. Finally, we will introduce the well-known Bagging algorithm: Random Forest. <h1>1. Prior Knowledge</h1> <h2>1.1 Introduction</h2> What is Ensemble? How effective is its capability? Before we dive into the various algorithms of Ensemble Learning, let’s establish its basic concept. In previous articles, we introduced various types of machine learning methods. In this article, we will focus on introducing a supervised learning technique known as Ensemble Learning. Unlike other common approaches, an Ensemble Learning model consists of a set of diverse classifiers, each with a different ability to make predictions. These diverse models are aggregated to create a stronger classifier, a process known as “ensembling,” and this entire learning process is referred to as Ensemble Learning. But just how effective is it? Ensemble Learning has made significant breakthroughs in research and has dominated various data competitions for a period. This dominance underscores the importance and strength of this method. <a href="https://medium.com/@Ryotess/ml-concept-ensemble-learning-i-bagging-random-forest-4d55ad414977">Website</a>