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When the interpretability of the models is preferred, we can use two rules to classify lung cancer data with 89.26% testing accuracy, and three rules to classify lung cancer data with a testing accuracy of 91.28%.
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Occasionally, other models were preferred, but this preference was never significantly greater than the gaze-related models.
Bifactor models were preferred on conceptual grounds.
Topic models are preferred for aspect extraction as coherent topics without requiring any manual support.
However, the logit and probit models are preferred over the LPM.
However, simple and robust models are preferred by engineers in practice.
Such complex models are relevant for control-oriented applications, but for applications such as trajectory prediction simpler models are preferred.
Dynamic reservoir models are preferred over exotic models in many fields in practice by the petrophysicist for calculating saturation.
As we show in our empirical results (Section 5), spatial models are preferred statistically in every case.
Animal models are preferred for studying molecular responses in mucosae during chemotherapy, but translation into clinical practice is difficult.
This sets the single-model DAC as a good choice for a base model in an ensemble, where usually shallow models are preferred as baseline models.
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