Random Forest

TL;DR

Builds many decision trees on different random subsets of the data.
Combines tree outputs by majority vote for classification or by averaging for regression.
Reduces variance and overfitting of a single decision tree; handles missing values and outliers but can be slower and more memory-intensive.

Definition

Random Forest is a machine learning algorithm that belongs to the ensemble learning method. It is used for classification and regression problems. It creates a forest of decision trees, where each tree is trained on a different set of data and makes a prediction. The final prediction is made by taking the average or majority vote of all the trees. This process helps in reducing the variance and overfitting of a single decision tree, resulting in a more robust model.

Explanation

The algorithm trains multiple decision trees, each on a randomly selected subset of the data. After training, each tree produces a prediction for a given input. For classification tasks the model selects the outcome with the majority of tree votes; for regression tasks it takes the average of the trees’ numerical predictions. Aggregating predictions across many trees reduces the variance and the tendency of a single decision tree to overfit, producing a more reliable model.

Examples

Example 1

Suppose we want to predict whether a person is likely to have a heart disease or not. For this, we have a dataset of various features such as age, blood pressure, cholesterol level, and so on. To predict the outcome, we can use a Random Forest algorithm.

Initially, the algorithm will randomly select a subset of the data and train a decision tree on it. Then, it will select another subset of the data and train another decision tree. This process is repeated until all the subsets are used to train the decision trees.

Now, suppose we have trained 10 decision trees on different subsets of the data. When a new test sample is given to the model, each tree will make a prediction on whether the person has a heart disease or not. If six out of the ten trees predict that the person has a heart disease, then the final prediction will be that the person has a heart disease.

Example 2

Consider a problem of predicting the salary of an employee based on his/her years of experience and education level. We can use a Random Forest algorithm to solve this problem.

The algorithm will again randomly select a subset of the data and train a decision tree on it. Then, it will select another subset of the data and train another decision tree. This process is repeated until all the subsets are used to train the decision trees.

Let’s say we have trained 10 decision trees on different subsets of the data. When a new test sample is given to the model, each tree will make a prediction on the salary of the employee. If six out of the ten trees predict a salary of $50,000, then the final prediction will be$ 50,000.

In both examples, the Random Forest algorithm has helped in reducing the variance and overfitting of a single decision tree. It has also provided a more robust and accurate prediction by taking the average or majority vote of all the trees.

Notes or pitfalls

Advantages of Random Forest:

It handles missing values and outliers well, making it a suitable algorithm for real-world data.
It can be used for both classification and regression problems.
It provides a feature importance measure, which helps in identifying the most important features in the dataset.
It has a high accuracy and low variance, making it a reliable algorithm.

Disadvantages of Random Forest:

It is a slow algorithm compared to other algorithms such as Logistic Regression or SVM.
It requires more memory and computational resources.
It may overfit on small datasets.

Decision tree
Ensemble learning
Logistic Regression
SVM
Feature importance