Decision trees work by recursively splitting the data into subsets based on the feature that results in the highest information gain. The process involves:
Selecting the best feature to split the data using metrics like Gini impurity or information gain. Creating a node representing the feature and branching out based on possible values. Repeating the process for each branch until a stopping criterion is met, such as a maximum depth or a minimum number of samples per leaf.
