IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

The primary goal during the training process of a decision tree is to minimize the impurity of the nodes. Decision trees aim to create branches that best separate the data according to the target variable. By focusing on minimizing impurity, which is often measured using metrics like Gini impurity or entropy, the tree is structured in a way that the resulting nodes contain samples that are more homogenous regarding the target outcome.

When a decision tree is trained, it assesses how well it can split the dataset at each node. The objective is to choose splits that lower the impurity the most, leading to branches that predominantly contain instances of a single class, especially in classification tasks. This process enhances the predictive power of the model because it systematically organizes the data into clearer segments, facilitating better decision-making based on the learned relationships.

As for the other options, they don't represent the primary objective in the same way. While maximizing the number of leaves, creating the shortest path, or balancing the depth might be considerations in some contexts, they do not align with the fundamental aim of achieving clarity and homogeneity through reducing impurity at each decision point.