Random forests — An ensemble of decision trees

Random forests — An ensemble of decision trees

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.

The motivation for random forests

First, we discuss some of the drawbacks of the Decision Tree algorithm. This will motivate you to use Random Forests.

  • Small changes to training data can result in a significantly different tree structure.
  • It may have the problem of overfitting (the model fits the training data very well but it fails to generalize for new input data) unless you tune the model hyperparameter of max_depth.

So, instead of training a single decision tree, it is better to train a group of decision trees which together make a random forest.

How random forests work behind the scenes

The main two concepts behind random forests are:

  • The wisdom of the crowd — a large group of people are collectively smarter than individual experts
  • Diversification — a set of _uncorrelated _tress

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.

supervised-learning data-science machine-learning decision-tree random-forest

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