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When evaluating the model we paid attention to the fact that the model was to be used for estimating mean differences and not for e.g. obtaining predictions.
Furthermore, when evaluating the model only with the transfers done towards estrous females, the relationship between mating success and sharing or not sharing meat disappeared (GLMM: estimate±sd = 0.23±0.43, t30 = 0.54, p = 0.59).
When evaluating the model performance by means of the root mean square error (RMSE), a measure of individual prediction errors (Table 3 ), the above conclusions were largely confirmed: the steady-state methods (Methods 1 and 2) provided the largest RMSE with overall larger errors of predictions based on FPG than those based on MPG.
When evaluating the models, it was determined that the soil is 300 400 m thickness and composed of more than one layers in parts where are especially closer to the bay.
Additionally, when evaluating the models, we are comparing the results against DHS-based estimates, which (although considered the current gold standard for most demographic and health indicators) have their own limitations.
There was no significant difference in the time required to successfully intubate when comparing DL and CVS techniques implying a similar level of competence when evaluated by this model.
This is far lower than the 90%% reported by Ott et al. when evaluating their model on synthetic data.
(The methodologies for performing internal and external validation are available in the literature [2].) When evaluating a model, it is clearly important to address the regulatory significance of errors in predictions, rather than simply their size.
If we use the observed amino acid frequency parameters of the dataset being examined (denoted by the '+F' suffix) instead, then we include 19 extra free parameters when evaluating each model.
However, for nominal data, the probability of occurrence can be predicted and compared to real data when evaluating a model, using a contingency table confusion matrix (Guisan and Zimmermann 2000), setting as a minimum acceptable model that correctly predicting over 70% of the data, which justifies the reliability of the model presented herein.
For example, the feature selection must be performed within each run of cross validation; and the nested cross validation is often required when evaluating model performance (i.e., outer loop CV for model performance evaluation and inner loop CV for model parameter tuning).
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