Sentence examples for models performed significantly from inspiring English sources

The 95% confidence interval upper limit AUC value of this distribution was then compared with the AUC of the actual model to assess whether the P. clarkii models performed significantly better than expected by chance alone (using a significant level of 0.05).

All models performed significantly better than random prediction (P<0.01 for all comparisons).

Compared to the radiologist, each AUC of the 5-fold cross-validation SVM models performed significantly better (P < 0.05) than the radiologist.

The PiPS-B models performed significantly better than did either the doctors (61.5% v 52.6%; P=0.0135) or the nurses (61.5% v 52.3%; P=0.012) but were not significantly better than the multi-professional estimate (61.5% v 53.7%; P=0.188).

OPLS-DA showed that all models performed significantly better (Q =0.42±0.1; RMSEP=0.37±0.1) than what is expected based on random chance (permuted: Q =-0.51±0.7; RMSEP=0.68±0.1).

Threshold-dependent one-tailed binomial tests on the Maxent extrinsic omission rate and proportional predicted area of the species distribution models (Fig. S1) were highly significant (Table 1), suggesting that the models performed significantly better than random.

All models perform significantly better at the mid-latitudes than at the Equator.

Computational results based on randomly generated data show that in the context of problems with modular costs, the proposed discretized models perform significantly better than the 'traditional' models.

The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks.

Where MAE statistics are available [40, 70, 71] weighted means were calculated, with the SCI general (22.7%) and activity specific (18.2%) models performing significantly better than the manufacturer's model (54.4%).

The results show that the identified models perform significantly well in the presence of noise and model uncertainties with the maximum error of 12%, thanks to the precise spectral analysis.