Machine Learning

Random Forests

An ensemble that combines predictions from many decision trees.

A random forest trains many decision trees using different samples of the training data and different subsets of features. Each tree makes a prediction, and the forest combines their results through voting or averaging.

This reduces the instability and overfitting associated with individual trees. Random forests work well on many tabular datasets, require limited preprocessing and can provide estimates of feature importance.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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