Artificial Intelligence Programming Practice Exam 2026 - Free AI Programming Practice Questions and Study Guide

Mutual information is a fundamental concept in feature selection that quantifies the amount of information that knowing the value of one variable provides about another variable. Essentially, it measures the statistical dependence between two variables; if two variables are independent, knowing one of them does not provide any information about the other, resulting in a mutual information value of zero. Conversely, a higher mutual information value indicates that knowing the value of one variable can significantly reduce uncertainty about the other variable, highlighting a strong relationship between them.

In the context of feature selection, mutual information is particularly useful because it helps identify which features contain relevant information for predicting the target variable. By selecting features that exhibit high mutual information with the target, models can potentially enhance their predictive accuracy and efficiency, as irrelevant features can introduce noise and complexity.

The other choices do not accurately reflect the purpose of mutual information within feature selection. For instance, the idea of information loss during model training or the focus on outliers and prediction accuracy does not relate to the direct measurement of information gain between variables. Similarly, while the total number of features is relevant in modeling, it does not represent what mutual information assesses. Therefore, the correct understanding of mutual information is pivotal for effective feature selection in machine learning and artificial intelligence.