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We performed analyses using SAS 9. Evaluating model performance Clinical prediction classifiers are commonly evaluated using a number of measures that quantify the models calibration and discrimination.

The most important of these is that a classifier that tries to maximize the accuracy of its classification rule when predicting a rare outcome may obtain an accuracy of 99 just by classifying all observations as non-events, and model improvements (e.

as quantified by increases in the c statistic) are overshadowed by the large true negative rate.