Which evaluation metric is most appropriate for a binary classification problem
Practice Questions
Q1
Which evaluation metric is most appropriate for a binary classification problem with imbalanced classes?
Accuracy
F1 Score
Mean Squared Error
R-squared
Questions & Step-by-Step Solutions
Which evaluation metric is most appropriate for a binary classification problem with imbalanced classes?
Step 1: Understand what binary classification means. It involves classifying data into two categories, like 'yes' or 'no'.
Step 2: Recognize what imbalanced classes are. This happens when one category has many more examples than the other, making it hard to evaluate performance.
Step 3: Learn about evaluation metrics. These are ways to measure how well your model is doing.
Step 4: Know the common metrics: Accuracy, Precision, Recall, and F1 Score.
Step 5: Understand Accuracy. It can be misleading in imbalanced classes because it might show high performance just by predicting the majority class.
Step 6: Learn about Precision. It measures how many of the predicted positive cases were actually positive.
Step 7: Learn about Recall. It measures how many actual positive cases were correctly predicted.
Step 8: Understand the F1 Score. It combines Precision and Recall into one number, giving a better overall picture of performance.
Step 9: Conclude that F1 Score is the best choice for imbalanced classes because it balances both Precision and Recall.
