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Bagging & Random Forest

What is Bagging?

Bagging, short for Bootstrap Aggregating, is an ensemble learning technique designed to improve the stability and accuracy of machine learning algorithms. It works by:

  1. Generating Multiple Datasets: It creates multiple subsets of the original training data through bootstrapping, which involves random sampling with replacement. This means that some observations may appear multiple times in a subset while others may not appear at all.

  2. Training Multiple Models: For each of these subsets, a separate model is trained. This can be any model, but decision trees are commonly used because they are prone to overfitting.

  3. Aggregating Results: Once all the models are trained, their predictions are aggregated to produce a final output. For classification tasks, the most common approach is to take a majority vote, while for regression, the average of the predictions is used.

What are Random Forests?

Random Forests is a specific implementation of Bagging that employs decision trees as the base learner. It adds an extra layer of randomness during the training process:

  1. Random Subset of Features: When constructing each decision tree, Random Forests selects a random subset of features for splitting at each node. This further decorrelates the trees and enhances the model's robustness.

  2. Aggregation: Just like in standard Bagging, Random Forests combine the predictions of all the individual trees to make a final prediction.

How Does Bagging Work in Random Forests?

  1. Bootstrapping: Create multiple bootstrapped datasets from the original training set.

  2. Building Trees: For each bootstrapped dataset:

    • Train a decision tree on the dataset.
    • At each node of the tree, randomly select a subset of features to determine the best split. This randomness helps ensure that the trees are less correlated with one another.

  3. Making Predictions:

    • For classification tasks, each tree in the forest votes for a class label, and the label with the majority vote is chosen as the final prediction.
    • For regression tasks, the final prediction is the average of the predictions made by all the trees.

Benefits of Random Forests

  • Reduced Overfitting: By averaging the results of many trees, Random Forests reduce the risk of overfitting that can occur with individual decision trees.
  • Robustness: The model is generally more robust to noise in the data.
  • Feature Importance: Random Forests provide insights into feature importance, helping identify which variables are most influential in predictions.

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