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

The class of an object in a decision tree is defined by the final leaf node that the object reaches. In a decision tree, each path from the root to a leaf node corresponds to a sequence of decisions based on the feature values of the input data. At the end of this path, the object is assigned to the class represented by that leaf node.

The leaf node typically contains information about the class label, which could be a category or a numerical value, depending on whether the decision tree is used for classification or regression tasks. During the training phase, the decision tree algorithm works to partition the data into subsets that lead to increasingly pure groups, ultimately resulting in leaf nodes that correspond to specific classes. Thus, when an object is processed through the tree, it reaches a leaf that assigns it its class based on the learned patterns from the training data.

The other choices do not accurately capture the way class assignments are made in a decision tree. The first split based on the most significant feature determines how the data is divided but does not define the object's final class. The average of all leaf node values pertains more to regression trees rather than directly defining class membership. Lastly, the depth of the tree affects the complexity of the model but does not determine the