The validation results indicated that all three models provided reasonably accurate predictions with errors in the range of 2.2 2.8%, and the modified Bottcher model (Al-Qadi, Lahouar and Leng (ALL) model) performed the best.
Since the average error can be dominated by a few predictions with large errors, the cumulative error distributions were plotted (Fig. 5).
Note that if the model is trained with uniform weights, our features can achieve a higher (R^{2}=0.47) and mean absolute error of 9.3 years, but at the expense of imbalance in predictions with higher error for less frequent ages.
To generate these curves, the set of chemical shift prediction errors was split into 100 bins of 0.01 ppm, plus a last bin containing predictions with an error equal or greater than 1 ppm.
Using reflectance data, our VisNIR penetrometer yielded quantitative SOC predictions with standard error of prediction (SEP = 0.19 log[g kg−1]) and ratio of prediction to determination (RPD = 2.34) highly similar to the ASD contact probe (SEP = 0.18 and RPD = 2.45).
It can be seen that larger n min values yield a higher number of accurate predictions (<0.2 ppm) and fewer predictions with high error (>1 ppm).
However, this case corresponds to predictions with no error, so we take as a proxy for the derivative at this point the value (l'=0).
Notably, the "all parameter" GRNN models using "only" five to six parameters provide very satisfactory predictions, with residual errors (varepsilon left( {theta,T_{i} } right)) less than 5% of the initial variability.
Satisfactory predictions with relative errors between 6 and 8% could be made for the relative amounts of internode, ear and leaf tissue.
The demonstration of OneR algorithm for sentiment prediction with smallest error of classification is given below: Step 1: Select a featured term from training set.
