What does a decision tree do at each node?

A decision tree operates by taking the dataset and organizing it into a tree-like structure that aids in making decisions. At each node of the tree, the algorithm evaluates specific features of the data to determine the best possible way to split the data. This is done based on the feature values, allowing the algorithm to maximize the separation of classes or minimize the impurity of the resulting groups.

The splitting process typically utilizes criteria such as Gini impurity, entropy, or variance reduction to decide how best to separate the data, ensuring that the resultant child nodes represent more homogenous subsets of the dataset in relation to the target variable. Thus, the strength of decision trees lies in their ability to manage and represent complex decision-making processes through these hierarchical splits at each node.

In contrast, while generating random splits is associated with some ensemble methods like random forests, a decision tree specifically evaluates the features of the data at each node. Classifying data based on the target variable happens at the terminal nodes, where predictions are made after the dataset has been split. Calculating the mean of the dataset is more relevant in regression scenarios but does not apply at each node of a decision tree, which focuses on splitting criteria instead.