Decision trees are a powerful machine learning algorithm that can be used for both classification and regression problems. They are a type of supervised learning algorithm, which means that they learn from labeled examples in order to make predictions on new, unlabeled data. In this blog post, we will explore the basics of decision trees, their applications, and a Python example. What are Decision Trees? A decision tree is a tree-like model of decisions and their possible consequences. It is a type of flowchart that is used to model decisions and their consequences. Each internal node in the decision tree represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a decision or prediction. The goal of the algorithm is to create a tree that can accurately predict the label of new data points. How do Decision Trees Work? The decision tree algorithm works by recursively partitioning the data into subsets based on the values of the inpu...