This is how decision trees are combined to make a random forest. In this article, I describe how this can be used for a classification task with the popular Iris dataset.
The Random Forest is one of the most powerful machine learning algorithms available today. It is a supervised machine learning algorithm that can be used for both classification *(predicts a discrete-valued output, i.e. a class) and *regression (predicts a continuous-valued output) tasks. In this article, I describe how this can be used for a classification task with the popular Iris dataset.
First, we discuss some of the drawbacks of the Decision Tree algorithm. This will motivate you to use Random Forests.
So, instead of training a single decision tree, it is better to train a group of decision trees which together make a random forest.
The main two concepts behind random forests are:
A random forest consists of a group (an ensemble) of individual decision trees. Therefore, the technique is called Ensemble Learning. A large group of uncorrelated decision trees can produce more accurate and stable results than any of individual decision trees.
When you train a random forest for a classification task, you actually train a group of decision trees. Then you obtain the predictions of all the individual trees and predict the class that gets the most votes. Although some individual trees produce wrong predictions, many can produce accurate predictions. As a group, they can move towards accurate predictions. This is called the wisdom of the crowd. The following diagram shows what actually happens behind the scenes.
Use of Decision Trees and Random Forest in Machine Learning. An Insight into Supervised Learning for Classification Problems
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
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In this article, we explore Decision Trees. Just like SVM, Decision Tree is capable of performing both classification and regression tasks. They are one of the most popular machine learning algorithms. The secret of its popularity lies within its simplicity. Also, they are able to produce results with a small amount of data and solutions it provides are easily explainable. This algorithm is exceptionally useful when it is used in ensemble learning, ie. they are an integral part of Random Forest, one of the most powerful machine learning algorithms. Random Forest is just a set of multiple Decision Trees, but more on that later. Back to Machine Learning Basics - Decision Tree & Random Forest
Comparison of supervised machine learning models to predict red wine quality in R. In the following project, I applied three different machine learning algorithms to predict the quality of a wine.