Explain Decision Trees.

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Decision Trees are a popular machine learning algorithm used for classification and regression tasks.

Decision Trees are a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.
They are easy to interpret and visualize, making them popular for exploratory data analysis.
Decision Trees can handle both numerical and categorical data, and can be used for both classification and regression tasks.
They work by recursively splitting the data based on the most significant attribute at each node, aiming to maximize information gain or minimize impurity.
Example: In a decision tree for predicting whether a customer will buy a product, the nodes could represent customer attributes like age, income, and location, with branches representing different thresholds or conditions.
Example: In a medical context, a decision tree could be used to predict the likelihood of a patient having a certain disease based on symptoms and test results.

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