Which model is known for predicting outcomes by splitting data into branches?

The model known for predicting outcomes by splitting data into branches is the Decision Tree. This type of model works by breaking down a dataset into smaller subsets while at the same time developing an associated decision tree. Each internal node of the tree represents a test on an attribute, each branch represents the outcome of that test, and each leaf node represents a class label or a continuous outcome.

Decision Trees are particularly useful for classification and regression tasks due to their intuitive representation that closely mimics human decision-making processes. Their structure allows for clear visibility into how decisions are made based on input features. This makes it easier for users to interpret the model's predictions and reason about the underlying data.

Other models mentioned, such as Neural Networks, Logistic Regression, and Bayesian Models, operate under different principles. For instance, Neural Networks use interconnected nodes (neurons) to learn patterns through layers, while Logistic Regression models the probability of a binary outcome through a logistic function. Bayesian Models apply Bayes’ theorem to update the probability estimates as new evidence is presented, without using a tree-based splitting method. Each of these models has unique strengths and weaknesses, making them suitable for different types of data and problems, but none employ the branching technique characteristic of Decision Trees.