Random Forest

Random Forest is an ensemble learning technique, which means it combines multiple individual models to make more robust and accurate predictions. This ensemble approach leverages the wisdom of the crowd by aggregating the predictions of multiple models, reducing the risk of overfitting, and improving overall performance.

Bagging: It employs a technique called bagging to create diverse training sets for each decision tree. Bagging involves random sampling with replacement from the original training dataset to create multiple subsets, often referred to as “bootstrap samples.” Each decision tree is trained on one of these bootstrap samples. This diversity helps prevent individual decision trees from overfitting to the training data.

Random Feature Selection: Another key feature of Random Forest is the random selection of features at each split node when constructing decision trees. Instead of considering all available features for the best split at each node, Random Forest randomly selects a subset of features to consider. This random feature selection reduces the correlation between trees and improves the model’s generalization ability.

Decision Tree Construction: Each decision tree in a Random Forest is constructed using the process described in the Decision Tree section. However, during tree construction, a random subset of features is considered at each node, which makes the trees decorrelated and reduces the risk of overfitting.

Classification and Regression: After building all the individual decision trees, Random Forest combines their predictions to make a final prediction. The method of combining predictions depends on the type of problem , For classification tasks, Random Forest uses a majority vote among the individual trees. The class that receives the most votes is the final prediction.For regression tasks, the final prediction is the average of the predictions from all the trees.

Advantages of Random Forest:

  • Improved predictive accuracy compared to individual decision trees.
  • Robust to noisy data and overfitting.
  • Can handle both classification and regression tasks.
  • Provides feature importance information.
  • Works well “out of the box” with minimal hyperparameter tuning.


  • Can be computationally expensive, especially for a large number of trees.
  • Interpretability can be challenging when dealing with a large number of trees.
  • May not perform well on highly imbalanced datasets.
  • Requires more memory and storage compared to a single decision tree.


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