Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputs the mode of the classes (classification) or the average prediction (regression) of the individual trees.
Construction of a Random Forest
- Decision Trees: The basic unit of a Random Forest is a decision tree, which is built recursively by splitting the data based on feature values to create nodes that best differentiate the classes or predict the target.
Randomization
Instead of using the entire dataset to construct a single tree, Random Forest introduces randomness in two ways:
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Random Feature Selection: At each node of every tree, a subset of features is randomly selected. This process aims to decorrelate the trees by reducing the influence of dominant features.
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Bootstrap Aggregating (Bagging): Each tree is trained on a random subset of the dataset created by sampling with replacement. This means some samples might be repeated in a subset, while others might not be included.
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Combining Trees: Once all the trees are constructed, predictions are made by averaging the predictions of all individual trees (regression) or selecting the mode (classification).
Key Principles
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Random Feature Selection: Helps prevent individual trees from being correlated, resulting in a more diverse set of trees.
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Bootstrap Aggregating: Reduces variance and overfitting by training each tree on slightly different data.
Advantages of Random Forests
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Handling Overfitting: Random Forests are less prone to overfitting due to the randomness introduced by feature selection and bagging.
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Feature Importance: They provide a measure of feature importance by evaluating how much each feature contributes to decreasing impurity across all trees.
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Robustness to Noisy Data: Random Forests are robust to noisy data because they consider multiple trees and aggregate their predictions.
Limitations and Scenarios
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Computationally Expensive: Constructing multiple trees can be time-consuming and resource-intensive, especially with large datasets.
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Black Box Model: Interpreting the model might be challenging due to its complexity.
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Not Suitable for Linear Relationships: Random Forests might not perform well when the relationship between features and target is primarily linear.
When to Choose Other Ensemble Methods
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Gradient Boosting: Better for optimizing predictive accuracy as it sequentially improves weak learners.
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AdaBoost: Useful when dealing with imbalanced datasets as it focuses on misclassified samples.
Random Forests are versatile and effective for various types of data but might not always be the optimal choice depending on specific requirements or dataset characteristics.