Which evaluation metric is most appropriate for a regression problem?
Practice Questions
Q1
Which evaluation metric is most appropriate for a regression problem?
Accuracy
F1 Score
Mean Absolute Error
Confusion Matrix
Questions & Step-by-Step Solutions
Which evaluation metric is most appropriate for a regression problem?
Step 1: Understand what a regression problem is. It involves predicting a continuous value, like predicting house prices or temperatures.
Step 2: Learn about evaluation metrics. These are ways to measure how well your predictions match the actual values.
Step 3: Identify common evaluation metrics for regression. Some examples include Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
Step 4: Focus on Mean Absolute Error (MAE). It calculates the average of the absolute differences between predicted values and actual values.
Step 5: Understand why MAE is useful. It gives a clear idea of how far off predictions are, without squaring the errors, which can exaggerate larger errors.
