Ensemble Learning: Random Forest


Random forest is an ensemble learning algorithm that combines the outputs of multiple decision trees to make predictions. It is a powerful and versatile algorithm that can be used for both classification and regression tasks.

Random forests work by training multiple decision trees on different subsets of the data. Each decision tree is trained using a different random sample of the data, and each tree is allowed to consider only a random subset of the features. This helps to reduce overfitting and improve the generalization performance of the model.

Once the decision trees are trained, they are used to make predictions on new data. For classification tasks, the class with the most votes from the decision trees is predicted. For regression tasks, the average prediction of the decision trees is returned.



Here are some of the advantages of using random forests:

  • They are very accurate and robust to overfitting.
  • They can be used to solve both classification and regression problems.
  • They are easy to interpret and understand.
  • They can be trained on large datasets relatively quickly.

Here are some of the disadvantages of using random forests:

  • Computationally expensive to train
  • Difficult to interpret
  • Sensitive to the choice of hyperparameters