How is a random forest different from decision trees?

Question

Accepted Answer

Random forest is an ensemble learning method that uses multiple decision trees to improve prediction accuracy.

Random forest builds multiple decision trees and combines their predictions to reduce overfitting.
Decision trees are prone to overfitting and can be unstable, while random forest is more robust.
Random forest can handle missing values and categorical variables better than decision trees.
Example: Random forest can be used for predicting customer churn in a telecom company.
Example: Decision tree can be used for predicting whether a patient has a certain disease based on symptoms.

Accepted Answer

A decision tree is a tree-based predictive model used for either regression or classification.

The decision tree splits the data based on features and segregates the dependent variable into multiple groups. The decision tree uses all the observations and all the features from training data to build the tree.

Random forest on the other hand is based on decision trees and uses multiple decision trees to get the final prediction. To build an individual tree in a random forest, partial data is used. Observations are sampled randomly from data and features are also randomly selected for every tree. This is called bagging. The final prediction is an aggregation of all the trees in a random forest.

Random forest models will have lower variance in prediction due to bagging. Decision trees have a tendency to overfit the training data, but the random forest can handle overfitting.

How is a random forest different from decision trees?

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