What is a random forest?

Question

Accepted Answer

A random forest is an ensemble learning method that combines multiple decision trees to make predictions.

Random forest is a supervised learning algorithm.
It can be used for both classification and regression tasks.
It creates multiple decision trees and combines their predictions to make a final prediction.
Each decision tree is trained on a random subset of the training data and features.
Random forest reduces overfitting and improves accuracy compared to a single decision tree.
It can handle large datasets with high dimensionality.
Random forest can provide feature importance measures.
Example: Predicting whether an email is spam or not based on various features.

Accepted Answer

Random forest is based on decision trees and uses multiple decision trees to get the final prediction. To build an individual tree in a random forest, partial data is used. Observations are sampled randomly from data and features are also randomly selected for every tree. This is called bootstrap sampling. Every decision tree in a random forest divides the data into different classes in the end. The decision tree has three types of nodes, the root node, the internal node, and the leaves. At the root and internal node, the tree divides data based on parameters like gini impurity index (for classification) or MSE (for regression). The final prediction is an aggregation of all the trees in a random forest. This is called bagging.

There is no loss function or cost function in a random forest.

Random forest models will have lower variance in prediction due to bagging.

What is a random forest?

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