A decision tree works like a detective asking a series of yes/no questions to isolate a target. Each question, or split, is chosen to maximize information gain by reducing impurity, measured with metrics like Gini impurity or entropy. The algorithm recursively partitions data until pure leaf nodes are reached, creating a hierarchical model that's inherently interpretable.

The primary challenge is overfitting, where a tree becomes too complex and memorizes training noise instead of learning general patterns. To prevent this, developers use pruning techniques like setting `max_depth` or `min_samples_leaf`. This makes decision trees foundational for powerful ensemble methods like Random Forests and Gradient Boosting Machines (GBMs).

While simple to visualize, single trees are unstable and prone to high variance. Their real power emerges in ensembles, where many trees vote or correct each other's errors. This architecture underpins modern tools like scikit-learn's DecisionTreeClassifier and winning solutions on platforms like Kaggle, balancing interpretability with predictive performance.