# Quantification (ML training)

Create quantification models using regression algorithms.

Metric

Description

Look for

RSquared

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. A score of 0.00 means the model is guessing the expected value for the label. A negative R2 value indicates the fit does not follow the trend of the data and the model performs worse than random guessing. This is only possible with non-linear regression models or constrained linear regression. R2 measures how close the actual test data values are to the predicted values.

The closer to 1.00, the better quality. However, sometimes low R-squared values (such as 0.50) can be entirely normal or good enough for your scenario and high R-squared values are not always good and be suspicious.

Mean absolute error

Mean absolute error (MAE) or Absolute-loss 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.

The closer to 0.00, the better quality. The mean absolute error uses the same scale as the data being measured (is not normalized to specific range). Absolute-loss, Squared-loss, and RMS-loss can only be used to make comparisons between models for the same dataset or dataset with a similar label value distribution.

Mean Squared Error

Mean Squared Error (MSE) or Squared-loss, also called 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 E) and squaring them. The squaring gives more weight to larger differences.

It is always non-negative, and values closer to 0.00 are better. Depending on your data, it may be impossible to get a very small value for the mean squared error.

Root Mean Squared Error

Root Mean Squared Error (RMSE) or RMS-loss (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. 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. Root mean square error is commonly used in climatology, forecasting, and regression analysis to verify experimental results.

It is always non-negative, and values closer to 0.00 are better. RMSD is a measure of accuracy, to compare forecasting errors of different models for a particular dataset and not between datasets, as it is scale-dependent.

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