Which evaluation metric is most appropriate for imbalanced classification proble
Practice Questions
Q1
Which evaluation metric is most appropriate for imbalanced classification problems?
Accuracy
F1 Score
Mean Squared Error
R-squared
Questions & Step-by-Step Solutions
Which evaluation metric is most appropriate for imbalanced classification problems?
Step 1: Understand what imbalanced classification problems are. This means that one class (like 'yes' or 'no') has many more examples than the other class.
Step 2: Learn about evaluation metrics. These are ways to measure how well a model is performing.
Step 3: Know that common metrics include accuracy, precision, recall, and F1 Score.
Step 4: Realize that accuracy can be misleading in imbalanced problems because a model can be accurate by just predicting the majority class.
Step 5: Understand precision, which measures how many of the predicted positive cases were actually positive.
Step 6: Understand recall, which measures how many actual positive cases were correctly predicted.
Step 7: Learn that the F1 Score combines precision and recall into one number, giving a better overall picture of model performance in imbalanced situations.
Step 8: Conclude that the F1 Score is the most appropriate metric for imbalanced classification problems because it balances both precision and recall.
