Branching out for answers.
A decision tree is a flowchart-like model that makes predictions by splitting data into branches based on yes/no questions.
Imagine you're deciding what to eat for dinner. You ask yourself: 'Do I want something hot or cold?' If hot, then 'Fast or slow to make?' Each answer leads to another question until you reach a final choice. A decision tree works the same way, asking simple questions to sort data into categories.
Decision trees are used everywhere from spam filters in email to medical diagnosis, because they're easy to understand and explain. They help computers make decisions the same way humans do—step by step.
Many people think decision trees always give perfect answers, but they can overfit—meaning they memorize the training data too well and fail on new data. Also, they're not always the most accurate model, but they're great for getting a quick, interpretable result.
A decision tree is a supervised learning algorithm that partitions the feature space into regions using a tree structure of internal nodes (tests on features) and leaf nodes (predictions). It recursively splits data based on the best feature according to a metric like Gini impurity or information gain, until a stopping criterion is met.