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Random Forest

An ensemble learning method that operates by constructing a multitude of decision trees during training and outputting the average prediction of the individual trees.

Categoryai

Overview

Random Forest is one of the most popular and versatile machine learning algorithms. It improves upon the accuracy and stability of a single decision tree by using a 'forest' of them.

How it Works

  1. Bagging (Bootstrap Aggregating): Each tree is trained on a random subset of the data.
  2. Feature Randomness: At each split in a tree, only a random subset of features is considered.
  3. Voting/Averaging: For classification, the forest takes a majority vote; for regression, it takes the average.

Advantages

  • Highly accurate and robust to outliers.
  • Handles both categorical and numerical data.
  • Provides a measure of 'feature importance.'
  • Less prone to overfitting than individual decision trees.

Related Terms

Gradient Boosting