Overall, ELM was recommended as the best model for Epan estimation when all the selected climatic data are available, while temperature-based PSO-ANN is recommended in MPZ and SMZ and temperature-based GA-ANN is recommended in TCZ and TMZ when the other climatic data are missing.
Consistently, with application in other field of analysis [ 28– 32], the evaluation of the best model for cost estimation of diabetes is not immediate.
Conversely, the best model for basal area estimation used the ND54 index as the predictor variable and was able to explain more than 80% of the variation in this attribute, confirming the explanatory power of spectral data for basal area estimation in Eucalyptus stands.
Conversely, several characteristic times and a dimensionless pressure ratio were established in order to define general quantitative criteria able to determine the best model for reliable KLaL estimation from dynamic oxygen response curves with the aim to contribute to design and scale-up of bubble column bioreactors.
If the number of components as well as the number of degrees of freedom are selected using the BIC, the best model for the LMs with unregularized estimation has 13 components and for LAMs with regularized estimation 16 components.
We also employed MrModeltest 2.2 (Nylander 2004) to determine the best model for use in Bayesian phylogeny estimation.
Model averaging has been shown to perform better than algorithms that use the single best model for prediction because it gives a truer estimation of uncertainty [ 27].
Model averaging has been shown to perform better than algorithms which use the single best model for prediction (such as AIC or BIC) because it gives a truer estimation of uncertainty [24].
Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation.
Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that GA always provide the best solutions and estimation.
