Articles on: Algorithms

Random Forest

Random Forest is an ensemble machine learning algorithm that is based on decision trees. The basic idea behind random forest is to combine multiple decision trees in order to reduce overfitting and improve the overall performance of the model.

The algorithm works by training a large number of decision trees on different subsets of the data, and then averaging the predictions of all the trees to make a final prediction. The subsets of data used to train each tree are selected randomly, with replacement, from the original dataset. This process is known as bootstrapping.

Each tree in the forest is grown using a different random subset of the data, and at each node, a random subset of the features is used to make the best split. This process is known as random subspace method.

A Random Forest Sample

When making a prediction, each tree in the forest casts a "vote" for the class or value that it predicts, and the final prediction is made based on the majority vote. In classification problems, this is known as majority voting, and in regression problems, it is known as averaging.

The randomness in the selection of subsets of data and features, along with the averaging of predictions, helps to decorrelate the trees, reducing overfitting and increasing the overall accuracy of the model.

Random Forest algorithm has several advantages over decision tree algorithms, such as better accuracy, ability to handle large datasets and high dimensionality, and resistance to overfitting. It also has the ability to give feature importance measure which can be used for feature selection.

Updated on: 27/01/2023

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