The chapter introduces decision trees, a versatile and intuitively interpretable tool for classification and regression tasks. Decision trees represent decisions through a hierarchical structure of nodes, branches, and leaves, with each path from root to leaf reflecting a decision-making process. The chapter introduces key concepts, including splitting decisions guided by criteria such as Gini impurity, entropy, and variance reduction, and discusses methods for handling categorical and continuous variables. A common challenge of model overfitting is examined, and strategies to address it, such as pruning and limiting tree depth, are discussed. The text also introduces ensemble methods, such as random forests, to improve the decision trees’ robustness and accuracy by aggregating predictions from multiple trees. The Python lab demonstrates an AI-assisted decision tree analysis of factors influencing travel intentions to an adversary country. The lab also demonstrates ways of dealing with typical challenges of the analysis: unbalanced datasets and overfitted models. The data comes from a survey of prospective Russian travelers to the US.