Which metric would you use to evaluate a model that predicts whether an email is
Practice Questions
Q1
Which metric would you use to evaluate a model that predicts whether an email is spam or not?
Mean Squared Error
Accuracy
F1 Score
R-squared
Questions & Step-by-Step Solutions
Which metric would you use to evaluate a model that predicts whether an email is spam or not?
Step 1: Understand the problem - We want to predict if an email is spam or not.
Step 2: Know the terms - Precision is how many of the predicted spam emails are actually spam. Recall is how many of the actual spam emails were correctly predicted.
Step 3: Recognize the challenge - In spam detection, there are usually more non-spam emails than spam emails, creating an imbalanced dataset.
Step 4: Choose the right metric - The F1 Score combines precision and recall into one number, making it useful for evaluating the model's performance in this scenario.
Step 5: Conclude - Use the F1 Score to evaluate the spam detection model.
