How does a Random Forest algorithm work?

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The Random Forest algorithm is an ensemble machine learning method used for classification and regression. It builds on decision trees but improves accuracy and reduces overfitting by combining multiple trees.

How it works:

1. Creating multiple trees:
   Instead of building one decision tree, Random Forest builds many (often hundreds) of trees, each trained on a random subset of the training data. This is called bootstrap sampling or bagging — sampling with replacement.

2. Random feature selection:
   At each split in a tree, a random subset of features (variables) is considered for splitting, rather than all features. This adds more diversity among the trees and reduces correlation between them.

3. Growing each tree fully:
   Each decision tree is grown to its maximum depth without pruning, capturing different patterns in the data.

4. Making predictions:

   • For classification, each tree votes for a class, and the forest selects the class with the most votes (majority voting).

   • For regression, the forest averages the predictions from all trees.

Benefits:

• Reduces overfitting compared to a single decision tree.

• Handles large datasets and many features well.

• Works well with missing data and maintains accuracy.

Summary:

Random Forest works by creating a "forest" of diverse, fully grown decision trees using random data and features. Combining their results improves robustness, accuracy, and generalization, making it a powerful and popular machine learning algorithm.

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