Which evaluation metric is best for regression tasks?
Practice Questions
1 question
Q1
Which evaluation metric is best for regression tasks?
Accuracy
Mean Absolute Error
F1 Score
Recall
Mean Absolute Error is commonly used to evaluate the performance of regression models.
Questions & Step-by-step Solutions
1 item
Q
Q: Which evaluation metric is best for regression tasks?
Solution: Mean Absolute Error is commonly used to evaluate the performance of regression models.
Steps: 6
Step 1: Understand what regression tasks are. Regression tasks involve predicting a continuous value, like predicting house prices or temperatures.
Step 2: Learn about evaluation metrics. Evaluation metrics help us measure how well our model is performing.
Step 3: Identify common evaluation metrics for regression. Some common metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
Step 4: Focus on Mean Absolute Error (MAE). MAE measures the average absolute difference between predicted values and actual values.
Step 5: Understand why MAE is useful. MAE is easy to interpret and gives a clear idea of how far off predictions are from actual values.
Step 6: Conclude that MAE is commonly used for evaluating regression models due to its simplicity and effectiveness.
