Which evaluation metric is most appropriate for assessing the performance of a l
Practice Questions
Q1
Which evaluation metric is most appropriate for assessing the performance of a linear regression model?
Accuracy
F1 Score
Mean Absolute Error (MAE)
Confusion Matrix
Questions & Step-by-Step Solutions
Which evaluation metric is most appropriate for assessing the performance of a linear regression model?
Step 1: Understand that a linear regression model predicts a continuous outcome based on input features.
Step 2: Know that evaluation metrics help us measure how well the model is performing.
Step 3: Learn about different evaluation metrics, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
Step 4: Recognize that Mean Absolute Error (MAE) calculates the average absolute difference between predicted values and actual values.
Step 5: Understand that MAE is easy to interpret because it gives the error in the same units as the predicted values.
Step 6: Conclude that MAE is a suitable metric for evaluating the performance of a linear regression model because it provides a clear measure of prediction accuracy.
