How Decision Trees Learn If-Else Rules to Solve Real-World Problems

Decision Trees are a supervised machine learning method that models decisions as a series of conditional if-else rules rather than fitting a straight line to data. The algorithm works by repeatedly splitting data into smaller, purer groups by selecting the question at each node that yields the highest Information Gain, often measured using Gini Impurity. New data points are classified by traversing the tree from root to a leaf node, where the prediction reflects the majority class of training examples that landed there. Without constraints, trees tend to overfit by memorizing training data, so parameters like max_depth and min_samples_leaf are used to limit growth and improve generalization. Decision Trees are widely applied in areas such as fraud detection, credit approval, medical diagnosis, and customer churn prediction, and are valued for their interpretability and minimal preprocessing requirements.