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            Regression Metrics

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            Regression Metrics

            Metrics for regressions help evaluate the performance of a model that predicts numerical values for continuous data.

            Predicted vs Actual

            The predicted vs actual scatter plot contrasts the predicted results of the model against the actual data results. The proximity of the points to the diagonal line indicates the accuracy of the model, with closer points representing higher accuracy.

            Predicted versus actual chart

            Residuals

            The scatter plot of residuals shows the variance between the actual and predicted values, known as residuals, in relation to predicted values. Ideally, the points should appear randomly scattered. On the y-axis, positive values indicate predictions that were too low, negative values indicate predictions that were too high, and values at zero indicate predictions that were exactly correct.

            Residuals chart

            4-Fold Cross Validation Results

            The 4-fold cross-validation approach mitigates sampling bias during the model validation process. In this method, the data is randomly divided into four separate partitions of equal size, and the model undergoes four test passes (folds). During each pass, three partitions serve as the training data, while the remaining one serves as the test data. By completing four test passes, each partition is used once as the validation data and three times as part of the training data, ensuring a comprehensive evaluation. Refer to the table of validation results to examine metrics corresponding to each fold of the data.

            Cross validation metrics table

            Metric Description
            Number of records

            Total number of observations. The meaning of a value varies per column.

            • For the Training Data and Validation Data columns, the numbers represent the total number of observations in the entire data used in the creation of the model for training and validation, respectively.
            • For the Fold #1 through Fold #4 columns, this value represents how many observations fell in that fold (approximately 25% of the entire data).
            R2

            R2 measures the model's ability to explain variation in the outcome, which is an indicator of how predictive the model is.

            Range:

            • 0 means that the model is not able to explain any variability in the outcome (random chance).
            • 1 means that the model explains all of the variability (perfect model).
            MAE Mean Absolute Error. Measures the absolute difference between the actual value and the prediction. All differences are weighted equally in this average, which means that it is not as sensitive to outliers as MSE.
            MSE Mean Squared Error. Measures the average squared error of the model’s predictions. MSE computes the square difference between the observed (actual) outcome and the predicted values, and then averages them.
            RMSE Root Mean Squared Error. Represents the square root of MSE. RMSE measures the difference between the values predicted by the model and the observed (actual) values. You can think of this value as the “standard deviation of errors.”
             
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