Which evaluation metric is most appropriate for a model predicting rare events?
Practice Questions
Q1
Which evaluation metric is most appropriate for a model predicting rare events?
Accuracy
Recall
F1 Score
Mean Squared Error
Questions & Step-by-Step Solutions
Which evaluation metric is most appropriate for a model predicting rare events?
Step 1: Understand what rare events are. Rare events are occurrences that happen infrequently in a dataset, such as fraud detection or disease outbreaks.
Step 2: Learn about evaluation metrics. These are tools used to measure how well a model performs.
Step 3: Identify common evaluation metrics. Some common metrics include accuracy, precision, recall, and F1 score.
Step 4: Focus on recall. Recall measures the ability of a model to find all the relevant cases (positive instances) in the dataset.
Step 5: Understand why recall is important for rare events. Since rare events are infrequent, it is more important to capture as many of them as possible, even if it means having some false positives.
Step 6: Conclude that recall is the most appropriate metric for evaluating models predicting rare events.
