A decision tree builds classification (or regression) models as a tree structure, with datasets broken up into ever-smaller subsets while developing the decision tree, literally in a tree-like way with branches and nodes. Decision trees can handle both categorical and numerical data.
In the context of a machine learning interview question, the correct answer to “What is Decision Tree Classification?” would typically involve a concise explanation of the concept:
“Decision Tree Classification is a supervised learning algorithm used for both classification and regression tasks. It works by recursively partitioning the input space into regions, where each partition corresponds to a decision node in a tree-like structure. At each decision node, the algorithm selects the feature that best separates the data based on certain criteria (such as Gini impurity or information gain). This process continues until a stopping criterion is met, resulting in a tree that can be used to predict the class label of unseen instances.”
This answer provides a brief overview of what Decision Tree Classification is, highlighting its key characteristics and how it operates in the context of machine learning tasks. Depending on the depth of knowledge expected in the interview, you might elaborate further on topics such as the splitting criteria, handling of categorical variables, handling of missing values, pruning techniques, or ensemble methods like Random Forests and Gradient Boosted Trees that are based on Decision Trees.