Satunnaismetsät

Satunnaismetsät, often translated as random forests, are a popular ensemble learning method used in machine learning for classification and regression tasks. They are based on the principle of building multiple decision trees during training and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

The core idea behind random forests is to reduce the overfitting problem that can occur with single decision trees. Each tree in the forest is trained on a random subset of the training data, selected with replacement (a technique called bootstrapping). Additionally, at each node split in the decision tree, only a random subset of the features is considered for finding the best split. This randomness in both data selection and feature selection helps to create a diverse set of trees, which in turn leads to a more robust and accurate overall model.

When making a prediction, a random forest aggregates the predictions from all the individual trees. For classification, the final prediction is the class that receives the majority of votes from the trees. For regression, it is the average of the predictions from all the trees. This averaging or voting process helps to smooth out the predictions and improve generalization to unseen data.

Random forests are known for their ability to handle large datasets and a high number of features. They are also relatively insensitive to outliers and can work with missing values to some extent. Their performance often rivals or surpasses that of single decision trees, and they are a valuable tool in the machine learning practitioner's toolkit.