A data scientist is evaluating a machine learning model for predicting customer satisfaction scores on a scale of 1 to 5. The model's predictions are compared against the actual scores. The goal is to measure the average magnitude of the errors without considering their direction (i.e., whether the prediction was too high or too low). Which evaluation metric is most appropriate for this scenario?
- AMean Absolute Error (MAE)
- BMean Squared Error (MSE)
- CR-squared (R²)
- DRoot Mean Squared Error (RMSE)
Show answer & explanationAnswer & explanation
Correct answer: A. Mean Absolute Error (MAE)
Mean Absolute Error (MAE) measures the average magnitude of the errors between predictions and actual observations, without considering their direction. This directly aligns with the requirement to measure 'the average magnitude of the errors without considering their direction'. MSE and RMSE penalize larger errors more heavily due to squaring, and R-squared measures the proportion of variance explained.
Why the other options are wrong
- B. MSE squares the errors, making it sensitive to outliers and penalizing larger errors more.
- C. R-squared measures the proportion of variance in the dependent variable that is predictable from the independent variables, not the raw error magnitude.
- D. RMSE is the square root of MSE, also sensitive to outliers and penalizing larger errors more.
Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is a regression metric that measures the average magnitude of the errors in a set of predictions, without considering their direction. It is the average of the absolute differences between prediction and actual observation.
- Calculated as Σ|actual - predicted| / n.
- Robust to outliers compared to MSE/RMSE.
- Provides an easily interpretable measure of average error magnitude.
- Units are the same as the target variable.
Memory trick: MAE: 'Magnitude Absolute Error' – just how big is the error, not if it's high or low.