IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

The method utilized for evaluating the performance of regression models is Mean Absolute Error (MAE). MAE measures the average magnitude of errors between predicted and actual values without considering their direction. It provides an intuitive understanding of how far the predictions deviate from the actual outcomes in a straightforward way, making it particularly useful in regression analysis.

MAE is calculated as the average of the absolute differences between predictions and actual outcomes. Its value indicates the average error in the same units as the target variable, allowing for easy interpretation of model performance. When evaluating a regression model, a lower MAE indicates better prediction accuracy.

In contrast, the other options are more suited to classification tasks. The confusion matrix is used to evaluate the performance of classification models by showing the true positives, false positives, true negatives, and false negatives. The ROC (Receiver Operating Characteristic) curve is a graphical representation of a classifier's performance at different threshold settings and is typically applied to binary classification problems. The Bernoulli distribution describes the outcomes of binary random variables, which are also not relevant for assessing regression models.