Explain Random Forest.

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🔹 How Random Forest Works

1. Bootstrapping Data (Bagging):

   • The algorithm creates multiple random subsets of the training data (sampling with replacement).

   • Each subset is used to train a separate decision tree.

2. Random Feature Selection:

   • At each node of a tree, instead of considering all features, a random subset of features is chosen.

   • This ensures trees are diverse and reduces correlation between them.

3. Building Multiple Trees:

   • Many decision trees are trained independently using different subsets of data and features.

4. Aggregation of Results:

   • For classification → Random Forest uses majority voting across trees.

   • For regression → It takes the average of all tree predictions.

🔹 Why Random Forest is Effective

• Reduces Overfitting: Individual decision trees can overfit, but combining many trees improves generalization.

• Robustness: Works well even with missing data or noisy datasets.

• Handles High-Dimensional Data: Effective when dealing with large numbers of features.

• Versatility: Can be used for feature selection, handling categorical and numerical data, and estimating feature importance.

🔹 Advantages

• High accuracy compared to a single decision tree.

• Resistant to overfitting due to averaging.

• Can measure feature importance for interpretability.

🔹 Limitations

• Slower for very large datasets compared to simpler models.

• Requires more memory since multiple trees are stored.

• Less interpretable than a single decision tree.

✅ In short:
Random Forest is an ensemble of decision trees where each tree is trained on a random sample of data and features. By combining their predictions, it achieves higher accuracy, reduces overfitting, and provides robust results.

Read More :

How does a decision tree algorithm work?

What are ensemble methods? Explain bagging and boosting.

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