Metrics to Calculate Performance of Machine Learning Algorithm – Continue

Regression Performance Metrics

Mean Absolute Error (MAE)

The Mean Absolute Error measures the average of the absolute difference between each ground truth and the predictions. Whether the predictions is 10 or 6while the ground truth was 8, the absolute difference is 2.

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Root Mean Squared Error (RMSE)

The Root Mean Squared Error measures the square root of the average of the squared difference between the predictions and the ground truth.

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R-Squared

R-squared is calculated by dividing the sum of squares of residuals (SSres) from the regression model by the total sum of squares (SStot) of errors from the average model and then subtract it from 1.

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Toufiq Mahmud