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5. Finally, the performance of that classifier is assessed on the validation set.
The performance of the final model was assessed on the validation dataset.
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For each method for calculating marginal risks, the model was fitted to each of the training datasets, and its predictive performance was assessed on the corresponding validation dataset.
AdvISER-PYRO hyperparameters were tuned on the validation dataset while performance was assessed on the test dataset.
Sample quality was assessed for the validation studies, but no samples were excluded from analysis based on low quality.
No penalty was assessed on the play.
No penalty was assessed on the hit.
Predictive performance and calibration of the model was assessed on an independent validation sample.
A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set.
Accuracy and bias, were assessed on both the fitting and validation datasets for both models.
Using cut-points determined by the derivation subsample, five risk categories were created on quintiles of predicted risk and graphically assessed in the validation sample.
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