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Next, classification and regression trees (CART) [ 19] were composed that divided the sample in subgroups as homogeneous as possible with regard to the outcome by selecting the most predictive variables based on minimum prediction error.
Then, considering minimum prediction error, an optimal artificial neural network was designed to predict the relative viscosity of the nano-lubricant.
Finally, the optimum result y is produced with the minimum prediction error [24].
The number of components was determined by cross-validation to have a minimum prediction error.
Beside the Least Squares class of methods, Minimum Prediction Error and Minimum Output Error methods have been investigated as well.
The minimum Prediction Residual Error Sum of Squares (PRESS) was 0.824 tested by leave one out cross validation.
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In recent years, due to the unusual solar minimum, predictions had to be corrected downward with every new indices update.
The resulting global minimum predictions are within 5 kJ/mol difference between the GLYGAL and Shape results, and the clusters around the interesting conformers are well sampled.
The confusion matrix for reported error rate as 0.73%% has been presented in Table 3 where class '9' shows maximum number of incorrect predictions as '14' and class '0' shows minimum incorrect prediction as '3'.
Hu et al. [23] presented an image data hiding scheme by using minimum rate prediction and optimized histogram modification method.
Furthermore, it gives better results especially for non-SPR channels compared to the well-known minimum nonlinear prediction error method and the simplified recursive algorithm developed in our previous work.
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