Sentence examples for blind dataset from inspiring English sources

The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method.

In the first round of tests, the only blind dataset I had at the time was barely big enough to meet minimum sample size requirements (it only had 48 companies).

This suggests that similarity reduction contributes to improved generalization on blind dataset.

To determine whether the reporter genes were robust across populations, we applied this metric to classify a separate blind dataset of mRNA relative quantities from a new population of CFS patients and healthy persons with limited success.

However, the PPV observed for the BLIND dataset is higher (from 79.8% for the final models compared with ≥85.3% PPV for the BLIND dataset), which might be an outcome of the unbalanced composition of the BLIND dataset.

Then, we measured the change in model performance on the blind dataset.

An important issue to consider when comparing the performance of trained/weighted computational prediction methods is the cross-validation dataset, that is, these prediction methods should ideally be tested using "blind" datasets to minimize the bias in the performances observed.

Systematic comparisons with the existing prediction models demonstrated that APCpred method significantly improved the prediction accuracy both in fivefold cross-validation of training datasets and in independent blind datasets.

However, non-consensus binding motifs are better covered by the methods introduced here since the consensus motifs I and II represent less than 30% of POS in both training and blind datasets.

Two individuals used blinded datasets to score rescue experiment images.

We applied this gene profile score metric to analyse a blind study dataset, selecting a profile score cut-off of 5, above which samples were classed as CFS disease and equal to or below were classed as healthy controls.