What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.
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A. B. C. D. E.AC
A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Incorrect Answers:
B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.
D: aucROC or area under the curve (AUC) is used to evaluate a classification model.
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metricsThe two metrics that can be used to evaluate a regression model are: A. Coefficient of determination (R2) C. Root mean squared error (RMSE)
A. Coefficient of determination (R2): The coefficient of determination (R2) is a statistical measure that represents the proportion of the variance in the dependent variable (Y) that is predictable from the independent variable (X). R2 ranges from 0 to 1, where 1 indicates that the model perfectly fits the data. R2 is often used as a goodness-of-fit measure for regression models. It measures the strength of the relationship between the predictor variables and the response variable. The R2 value can be interpreted as the proportion of the variation in the dependent variable that is explained by the independent variables. A higher R2 value indicates a better fit of the regression model to the data. However, it is important to note that a high R2 value does not necessarily mean that the model is accurate, and other metrics should be considered in conjunction with R2.
C. Root mean squared error (RMSE): RMSE is a measure of the difference between the actual and predicted values of the dependent variable. It measures the average magnitude of the error in predicting the outcome variable. RMSE is calculated by taking the square root of the mean of the squared differences between the predicted and actual values. The lower the RMSE value, the better the model. RMSE is a widely used metric in regression analysis, and it is useful for comparing different models as it provides a clear indication of the model's predictive accuracy.
B, D, and E are not appropriate metrics for evaluating a regression model. F1 score is a metric used to evaluate classification models, and AUC and balanced accuracy are used to evaluate binary classification models.