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Decision trees (DTs, [ 8]) are tree-shaped symbolic models with tests on the feature values in the internal nodes, and class labels in the leaves.
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The DCE was analysed using multinomial logit regression models with test-specific parameters.
By combining dynamic models with testing volume data to reproduce observed surveillance data, our results highlight the significant impact testing volume can have on observed case counts.
All statistical models in this step will be formulated as logistic regression models, with test status as response, and the disease status as explanatory variable [ 19].
The explanatory variables WIM and lnWIM were entered simultaneously in all models with test-day milk yield, lnSCC or CA as outcome variables.
To account for repeated sampling, we fitted generalized estimating equation (GEE) models with test fish ID and dominant pair ID as subject effects and repeats per test fish and repeats per dominant pair as within-subject effects.
Consistency of treatment effects in different subgroups was explored by Cox regression model, with tests for interaction.
Consistency of treatment effects in prespecified subgroups was explored by Cox regression model with tests for interaction.
However, few researchers pay attention to the construction of rough set model with test cost and decision cost simultaneously.
The model has been validated by comparing outputs from the model with test data, given in reference papers, derived from air-cooled condensers with differing dimensions and refrigerants.
The effect of the structure parameters of the hydrogen injector on the flow characteristics was studied after verification and validation of the model with test data.
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