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Why should it be the case that simple models have less black-box character than others?
Linear and logistic regression models have less potential for overfitting primarily because the range of functions they can model is limited.
In addition, CR models have less power than the Cox-PH models to rule out the same non-inferiority margin [ 30].
DOI: http://dx.xoi.org/10.7554/eLife.00051.004 > -wrap-foot> Altheugh the fits of the models were comparable, the parameters of the logistic and log-linear models have less obvious explanatory value.
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We found that smaller or less-expensive models have less-sophisticated ways of dealing with vacuum dynamics and steam.
Forty-eight percent of datasets used to evaluate the prediction models had less than a previously recommended minimum of 100 events [ 28].
The remaining 12% were covered less than 50% and of this set, 7% of the ORF models had less than 20% of their length verified by 454-reads.
Indeed this model has less than 5% AARD for more than 50% of the data points.
This comparison shows that the IR model has less model mismatch with the high order model regarding the relevant dynamics of these typical channels compared to the other simplified models.
Airbus faces an even tougher task, as its half of the proposed A340 fleet trade-in is about 15 years old on average, and the model has less range.
The results show that the ANFIS-PSO/GA model has less uncertainty in different hydraulic conditions of channels for predicting the vertical level of threshold bank profile and can predict satisfactorily the channel bank profiles.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com