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Regression Tester
Test a model for regression problems.
Inputs
| Name | ID | Description | Type |
|---|---|---|---|
| Model | Model | Trained Regression model. | Generic |
| Testing Data Set | Testing Data | The testing data set. | Generic |
Outputs
| Name | ID | Description | Type |
|---|---|---|---|
| Predictions | Predictions | Predictions | Generic |
| R2 | R2 | R-squared (R2, also known as the 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. A score of 0.00 means the model is guessing the expected value for the label. R2 measures how close the actual test data values are to the predicted values. | Double |
| Absolute Loss | Abs Loss | Absolute loss, also known as the Mean absolute error (MAE), measures how close the predictions are to the actual outcomes. It is the average of all the model errors, where model error is the absolute distance between the predicted label value and the correct label value. This prediction error is calculated for each record of the test data set. Finally, the mean value is calculated for all recorded absolute errors. | Double |
| Squared Loss | Sq Loss | Squared loss, also known as Mean square error (MSE) or Mean squared deviation (MSD), tells you how close a regression line is to a set of test data values by taking the distances from the points to the regression line (these distances are the errors\u2001E) and squaring them. The squaring gives more weight to larger differences. | Double |
| Root Mean Squared Loss | RMS Loss | Root mean squared error (RMSE) measures the difference between values predicted by a model and the values observed from the environment that is being modeled. RMS-loss is the square root of Squared-loss and has the same units as the label, similar to the absolute-loss though giving more weight to larger differences. | Double |
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