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1.Bootstrapping: It starts by randomly selecting a subset of the training data with replacement.


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  1. Bootstrapping: It starts by randomly selecting a subset of the training data with replacement. This subset is known as
    a “bootstrap sample.” Some data points may appear multiple times in the sample, while others may be omitted.

  2. Feature Randomness: At each node of each decision tree in the Random Forest, a random subset of features is
    considered for splitting. This introduces diversity among the trees. The number of features to consider at each node is
    typically less than the total number of features in the dataset.

  3. Building Decision Trees: The Random Forest creates a collection of decision trees, each trained on a bootstrap
    sample and using feature randomness. These trees are constructed using techniques like Gini impurity (for
    classification) or mean squared error (for regression) to determine the best attribute for splitting.

  4. Voting or Averaging: For classification tasks, the Random Forest combines the predictions of individual trees
    through majority voting. Each tree’s prediction is counted, and the class with the most votes becomes the final
    prediction. For regression tasks, the predictions are averaged.

  5. Ensemble Effect: The ensemble of decision trees helps reduce the variance and overfitting that individual trees might
    exhibit. By combining multiple trees with different perspectives, the Random Forest provides a more stable and
    accurate prediction.

  6. Out-of-Bag (OOB) Error: Random Forests use the out-of-bag error to estimate the performance of the model. This
    is done by evaluating each tree on the data points it didn’t see during its construction (left out due to the bootstrap
    sampling). The OOB error serves as a useful indicator of model performance.

  7. Feature Importance: Random Forests can also provide information about feature importance. This helps identify
    which features have the most influence on the p


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