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When multiple hypothesized models have predictions that are consistent with the measurements, experimental design is used to discriminate between the models.
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The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively.
The model has prediction accuracy of 100% for the experimental data and 81.94% for the literature data at a deviation level of ±7 °C.
The proposed model has prediction and diagnosis capabilities enabling decisions to be made on a project-by-project basis and is based on existing theoretical constructs.
For Plot 2 the two models have similar predictions; however, neither reflect the observed plot trajectory very well.
The results of the models for prediction of the UCS and BTS showed that the equations obtained from the regression models have high prediction performances and exhibited the more reliable predictions than the ANN model although it had been mentioned ANN models have higher prediction capacity [24], in addition UCS shows a higher correlation with Is 50) than BTS.
For predicting work, unemployment and recurrent sickness absence both models have better prediction accuracy than the null model.
The results reveal that the proposed models have good prediction capability with acceptable errors.
The results revealed that the proposed models have good prediction and generalization capacity with acceptable errors.
The results showed that the proposed models have excellent prediction ability with insignificant error rates.
The experimental results show that the part-load GT-Power models have sufficient prediction accuracy, with maximal error of 8.5%.
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CEO of Professional Science Editing for Scientists @ prosciediting.com