Regression Accuracy Metrics (Part 1): Machine Learning Interview Prep 19
Regression accuracy metrics are essential tools used to assess the performance of regression models, which predict continuous outcomes. These metrics quantify the difference between predicted values and actual values, thereby evaluating the model’s accuracy. These metrics guide model refinement and selection, ensuring reliable predictions.
Let’s check your knowledge of Supervised Learning — Regression Accuracy Metrics. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!
1. Which set of metrics are not used to measure the accuracy of a regression model?
(A) Coefficient of Determination (higher is better), R² (higher is better), Adjusted R² (higher is better).
(B) Mean Absolute Error (MAE) — (lower is better), Mean Absolute Percentage Error (MAPE)-(lower is better), Median Absolute Error-(lower is better).
(C) Mean Squared Error (MSE)-(lower is better), Mean Squared Log Error (MSLE)-(lower is better), Root Mean Squared Error (RMSE)-(lower is better), Root Mean Squared Percentage Error (RMSPE)-(lower is better).
(D) Accuracy-(higher is better), Precision-(higher is better), Recall-(higher is better), F1 Score-(higher is better), ROC-AUC-(higher is better).
2. Mean Absolute Error uses Manhattan distance to calculate the error. A lower MAE indicates a more accurate model. What does MAE measure?
(A) The average of the squared differences between predicted and actual values.
(B) The proportion of variance explained by the model
(C) The square root of the average of squared differences between predicted and actual values.
(D) The average of the absolute differences between predicted and actual values.
3. A lower RMSE indicates a more accurate model. Which of the following is true about RMSE?
(A) RMSE is less sensitive to outliers than MSE.
(B) RMSE can be negative.
(C) RMSE is always smaller than MSE.
(D) RMSE is the square root of MSE.
4. Which metric is most sensitive to outliers in a regression model?
(A) Mean Absolute Error (MAE)
(B) Mean Squared Error (MSE)
(C) Root Mean Squared Error (RMSE)
(D) Mean Absolute Percentage Error (MAPE)
5. What does R-squared (R²) in regression analysis represent?
(A) The ratio of the total sum of squares.
(B) The proportion of the variance in the dependent variable that’s explained by the independent variables.
(C) The mean squared error of the model.
(D) The average prediction error.
6. When Adjusted R-squared is used instead of R-squared?
(A) There are multiple independent variables.
(B) The model is a simple linear regression.
(C) The sample size is very large.
(D) The model accuracy is extremely high.
7. Which metric would be the best to use when you have a regression model that is sensitive to the scale of the data?
(A) Mean Squared Error (MSE)
(B) Mean Absolute Error (MAE)
(C) Coefficient of Determination
(D) Root Mean Squared Logarithmic Error (RMSLE)
8. Which metric is known as the coefficient of determination?
(A) Mean Absolute Error (MAE)
(B) Mean Squared Error (MSE)
(C) R-squared
(D) Root Mean Squared Error (RMSE)
9. Which option is correct?
(A) MSE, due to the squaring of each error, tends to heavily weigh larger errors, making it more sensitive to outliers compared to other metrics.
(B) Adjusted R-squared is preferred over R-squared in the presence of multiple independent variables as it adjusts for the number of predictors in the model.
(C) RMSE is in the same units as the response variable, making it more interpretable than MSE
(D) All of the above
10. Mean Squared Error uses Euclidean distance to calculate the error. A lower MSE signifies better model performance, indicating that the model’s predictions are closer to the true values. What does MSE measure?
(A) The average of the squared differences between predicted and actual values.
(B) The proportion of variance explained by the model
(C) The square root of the average of squared differences between predicted and actual values.
(D) The average of the absolute differences between predicted and actual values.
The solutions will be published in the next blog Regression Accuracy Metrics (Part 2): Machine Learning Interview Prep 20.
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The solution of Support Vector Machine (SVM) Part 2: Machine Learning Interview Prep 18: 1(A), 2(A), 3(B), 4(A), 5(C), 6(A), 7(B), 8(C), 9(D), 10(A).
References:
[1] R-squared, Clearly Explained!!!
[2] How to Calculate Accuracy for Regression?
[3] Beyond Accuracy: Understanding the Performance Metrics of Regression Models
[4] Regression Metrics
