AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

The decision tree algorithm excels at tasks where the goal is to predict categorical outcomes, particularly when decisions are made based on the values of several input features. This method works by splitting the data into subsets based on the value of the input features, creating a tree-like model of decisions. Each internal node in the tree represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label, which allows for clear interpretation and straightforward predictions.

In the context of predicting categorical outcomes, decision trees can handle both binary classifications and multi-class classifications effectively by creating paths that lead to a clear decision based on the features provided. This capability makes them particularly useful for problems where the outcome is discrete, such as classifying whether an email is spam or not, predicting customer segments, or diagnosing diseases based on symptoms.

The other options represent tasks for which decision trees may not be the most effective choice. Image segmentation, for instance, typically requires handling high-dimensional data and understanding spatial relationships, which decision trees do not manage as efficiently as other models like convolutional neural networks. Time series forecasting involves predicting future values based on past data, often requiring specialized techniques to capture temporal dependencies, which decision trees do not inherently incorporate. Lastly,