Sentence examples for evaluation of prediction from inspiring English sources

Exact(27)

The results from the evaluation of prediction accuracy showed that this method accurately predicted TS and DT, suggesting that it could construct a reliable response surface for TS and DT with a small number of samples.

Because our evaluation of prediction accuracy does not distinguish between inhibiting and activating interactions, the predicted interaction weights are computed as the absolute value of the correlation coefficients (4) SPEARMAN-C is a modification of Spearman's correlation coefficient where we attempted to favor hub nodes, which have many, strong interactions.

Genschow, E. et al. The ECVAM international validation study on in vitro embryotoxicity tests: results of the definitive phase and evaluation of prediction models.

This paper discusses methods employed by this software, and an evaluation of prediction accuracy for the period 1 March 2001 through 31 August 2001.

A wide variety of approaches to hydrologic (rainfall runoff) modeling of river basins confounds our ability to select, develop, and interpret models, particularly in the evaluation of prediction uncertainty associated with climate change assessment.

In addition to correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and t-test have been also used for evaluation of prediction accuracy on both ANNs and ML methods.

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Similar(33)

We recommend a modification of ROC that remedies these problems, using partial-area ROC approaches to provide a firmer foundation for evaluation of predictions from ecological niche models.

Although the plasticity of these sex differences will make it challenging to generalize from invertebrate to vertebrate systems, studies in nematodes have enabled empirical evaluation of predictions regarding the evolution of lifespan.

[Performance measures for prediction models and markers: evaluation of predictions and classifications].

The evaluation of predictions by existing models clearly demonstrates the need for improvement.

It is however essential for the critical evaluation of predictions and for the understanding of toxicological mechanisms.

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