Decision Tree Algorithms Concepts of Decision Trees Decision trees are machine learning algorithms that use a tree-like structure to model decisions and their possible consequences. The tree is made up of nodes, which stand in for decisions, and branches, which stand in for potential outcomes of those decisions. Each internal node corresponds to a test on an attribute, each branch corresponds to the outcome of the test, and each leaf node corresponds to a class label. For example, let's say we have a dataset of housing prices, including the number of bedrooms, square footage, and location of each house. We can use a decision tree to model the relationship between these attributes and the price of the home. Decision Trees Algorithm: Define the problem and collect data. Choose a hypothesis class (e.g., decision trees). Split the data into training and validation sets. Construct a decision tree by recursively splitting the data based on the most informative features. Prune the decisio