Which evaluation metric is best for regression tasks?
Practice Questions
Q1
Which evaluation metric is best for regression tasks?
Accuracy
Mean Absolute Error
F1 Score
Recall
Questions & Step-by-Step Solutions
Which evaluation metric is best for regression tasks?
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.
