We analyzed the predictive accuracy of the quadratic tuning model used in our n-th order unscented Kalman filter.
To further assess the predictive accuracy of our model, we analyzed a NSCLC cell line dataset assayed on Affymetrix U133A microarrays from Girard and colleagues (GEO GSE4824).
The predictive accuracy (fraction of correctly assigned samples) of each signature was analyzed as a function of the number of times a sample was assigned to the poor outcome group.
To evaluate the predictive accuracy of serum biomarkers, receiver operating characteristic (ROC) curve and the area under the curve (AUC) were analyzed.
The predictive accuracy The successful discretization algorithm should perform discretization such that the predictive accuracies are increased or without significant reduction of predictive accuracy.
Increasing the training period enhanced the predictive accuracy (Fig. 4B).
The predictive accuracy was assessed using multiple regression analysis.
The more data is used to train the model, the higher the predictive accuracy.
This paper investigates the predictive accuracy of stochastic models.
The predictive accuracy results of the dataset are summarized by the mean of its 10 folds.
