Sentence examples for better predictive performance for from inspiring English sources

Exact(4)

For instance, the AUC value of RF classifier for brain-specific gene prediction (AUC = 0.9488, Table 1) is higher than that for brain-selective gene prediction (AUC = 0.9178, Table 6), whereas the RF classifier gave better predictive performance for liver-specific gene prediction (AUC = 0.9777, Table 1) than liver-selective gene prediction (AUC = 0.9547, Table 6).

The results indicate that the RF classifiers achieved better predictive performance for tissue-specific gene prediction.

All genic regions considered delivered a better predictive performance for BM and an inferior prediction for BW and HHP.

We observed a slightly better predictive performance for CDS and Exons than for Genes and Genes1kb.

Similar(55)

However, the differences in ranking of some variables that were found to be significant may account for the better predictive performance of the ANN compared to the clinicians and suggest the need for further study.

Although the any-stage model including ultrasound (model 3) retained its R value of 0.54 in phase II, its value dropped to 0.30 for model 1, indicating a better predictive performance of models, including ultrasound evidence.

This explains the better predictive performance of BRANN over SCGANN: Bayesian regularization imposes a penalty for model complexity and prevents over-fitting, while SCGANN captures both signal and noise in the training set, leading to poor prediction.

This approach enables better predictive performance from a simpler model.

Presumably, structural variation data (e.g., copy number variations) in chicken will become available in the near future, but their contribution towards a better predictive performance may be limited for a trait like HHP, because such variations are observed only at particular regions in the genome.

However, the polynomial calibration equation had better predictive performance than the linear equation for the CTR-E200 meter.

In statistical learning, we can often achieve better predictive performance by combining the predictions from multiple models.

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