What measures how often a learning model incorrectly classifies positive outcomes?

The measure that assesses how often a learning model incorrectly classifies positive outcomes is the False Positive Rate. This metric specifically focuses on the instances where the model predicts a positive outcome, but the actual outcome is negative. In situations like binary classification, the False Positive Rate provides critical insights into the model's performance by highlighting the proportion of negatives that are incorrectly identified as positives.

A lower False Positive Rate indicates that the model is better at avoiding false alarms, which is particularly important in applications where false positives can have serious implications, such as in medical diagnoses or fraud detection.

In contrast, the True Negative Rate measures the correct predictions of negative outcomes, Precision indicates the accuracy of positive predictions considering only the correctly predicted positives, and Recall (or Sensitivity) assesses how well the model identifies actual positive outcomes from the total actual positives. Each of these other metrics provides valuable information about the model’s performance, but they do not focus specifically on the incorrect classification of positive outcomes as the False Positive Rate does.