Classifying using random forest and interpreting it with iml
Random forest is a versatile ML algorithm that can be used for both regression and classification tasks. It is an ensemble learning method that combines multiple decision trees to make predictions. Decision trees split the data based on the values of features to create subsets with similar target variable values. Random forest combines multiple decision trees to create a more robust and accurate model. The algorithm randomly selects a subset of the training data (bootstrapping) and a subset of features at each tree’s node to create a diverse set of decision trees. The random subsets of the training data are used to train individual decision trees in the forest. The bootstrapping technique allows each tree to see a slightly different variation of the data, reducing the risk of overfitting.
Random forest assesses feature (variable) importance by evaluating how much each feature contributes to reducing error in the...
