The two sets are considered appropriate if they follow unicolumn statistics i.e., maximum of the test set is less than maximum of training set and minimum of the test set is greater than of training set (Table 1).
The dataset division satisfied the criteria of an appropriate model; namely, the maximum of the test set was less than the maximum of the training set and the minimum of the training set was greater than the minimum of the test set.
As a result, the maximum size of the training set achieved the minimum MAE, which can be observed in Fig. 9.
Interestingly, the prediction performance for daily peak energy consumption for the next 7 days prediction interval is 59.93 times better than the baseline (MSE = 601.7531 vs baseline MSE = 36,062.7851, which is the training set maximum value).
Universal kriging parameters θ and coefficients α were estimated via profile maximum likelihood using the training set.
and are the minimum and maximum scores determined from a training set.
For the coefficients of Equation 13, β0 is computed on the training set using maximum likelihood parameter estimation on the regression function lnN c (t r ) = β0(t i,t r )+ lnN c (t i ) and σ 0 2 is the estimate of the variance of the residuals on a logarithmic scale.
The median mRNA expression level was 12.77 (minimum, maximum: 0.34, 71.88) for the training set and the same cutoff was used for the analysis of the validation and control sets.
Furthermore, the value for 'Maximum Omitted Features' (MOF) of a training set compound may be adjusted; when the value is set to 1, this implies that a 'partial fit' of this training set molecule is allowed.
