Sentence examples for predictive performance of the models from inspiring English sources

To evaluate the predictive performance of the models, cross-validation was performed using 1000 repeated random sub-samples of the data of the entire cohort divided in a 1 1 ratio into derivation and validation datasets.

To evaluate the predictive potential of these variables, the models were evaluated using cross-validation to assess the predictive performance of the models and further refine the variable included in a prediction analysis, so that the resulting models were evaluated on the testing data, as opposed to the training data.

For the practical validation, potential applications of the neural network models were analyzed, and the predictive performance of the models was assessed using ecological expert knowledge.

The sampling design strongly influenced the predictive performance of the models while the number of pseudo-absences had minimal effect on the predictive performance.

This is to establish the predictive value of the models, since validating the models on the same year they were learned would not have yielded any information about the predictive performance of the models on new unseen data.

The models here are binary classifiers and the following measures have been utilised to assess the predictive performance of the models based upon true positive (TP), false positive (FP), true negative (TN) and false negative (FN) result classification.

Criteria used for performing the predictive performance of the model were accuracy rate, positive predictive value, negative predictive value, false-positive rate, and false-negative rate computed from test data according to the LOOCV methodology.

In general, the predictive performance of the model increased after performing data sampling.

Finally, a categorical VPC for the frequently used dichotomized CTC count (<5 vs. ≥ 5) was performed and confirmed the acceptable predictive performance of the model (Supplementary Figure S3).

A multiple regression was performed to test the relation between the predictive performance of the model and the participants' accuracy for each of the 256 videos.

To examine the predictive performance of the model the differences between the predicted and observed EQ-5D scores at the individual level were examined by computing the mean squared error (MSE) and root MSE (RMSE).