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Evaluation of the models' performance was based on statistical indices obtained from a confusion matrix by using validation samples.
The models performance was stable in larger data sized problems.
Models' performance was assessed through discrimination and calibration.
The models' performance was compared by visually inspecting the ranges over which rates differed across severities and by using the AIC for model fit.
The primary basis for comparing the models' performance was the c-statistic (or area under the receiver operating characteristic (ROC) curve).
The task was formulated as a binary classification problem and the models' performance was tested on five large acceptor splice site datasets from five organisms.
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As is typical with predictive modeling, our models' performance is affected by the percentage of uncontrolled asthma in AST assessments.
Model derivation and assessment of model performance was performed by using R software version 2.6.0.
Further, the model performance was evaluated using various performance criteria.
Model performance was assessed using Monte Carlo techniques.
The C statistic was used to quantify discrimination, and model performance was internally validated using 10-fold cross-validation.
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