How does a random forest algorithm work?

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The Random Forest algorithm is an ensemble learning method that builds multiple decision trees and combines their outputs to improve accuracy and reduce overfitting. It's used for both classification and regression tasks.

🌲 How Random Forest Works:

1. Bootstrap Sampling (Bagging):

   • It creates multiple subsets of the training data by randomly sampling with replacement.

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

2. Feature Randomness:

   • At each split in a tree, only a random subset of features is considered.

   • This increases diversity among trees and reduces correlation between them.

3. Tree Construction:

   • Each tree grows independently, often to full depth (unpruned).

   • Trees are weak learners, meaning they might overfit on their own.

4. Prediction Aggregation:

   • Classification: The final prediction is the majority vote from all trees.

   • Regression: The final output is the average of all tree predictions.

✅ Advantages:

• Handles large datasets and high-dimensional features well

• Reduces overfitting compared to single decision trees

• Handles both categorical and numerical data

• Provides feature importance scores

⚠️ Limitations:

• Less interpretable than a single decision tree

• Can be slow with a large number of trees or high-dimensional data

🧠 Key Concepts:

• Bagging: Combines many models trained on different data

• Random feature selection: Makes trees less correlated

• Ensemble voting/averaging: Improves accuracy and generalization

In short, Random Forest builds many diverse decision trees and combines them to create a more robust and accurate model.

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