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Tuning the coefficients using the calibration set resulted in a penalization thereof, compared to the coefficients obtained when calibrating using only the feature selection set.
The entire data set was split into calibration set and prediction set according to soil type.
A calibration set comprises the multidimensional space that represents the samples for prediction.
We also tested 38 different calibration set sizes varying from 10 to 380 samples.
Probability model proved to be concordant in 91% of the calibration set observations.
The correlation coefficients of calibration set (Rc2) were 0.935 and 0.880, respectively.
A method for designing a calibration set in spectral space was developed.
Thirty-six mixtures were used in the calibration set and the others in the validation.
Using the newly developed spectral based method, 11 tablets were prepared for the calibration set.
In the proposed approach, initial centers are randomly selected from the calibration set.
One prediction set contained similar information to calibration set while the other prediction sets contained different information from calibration set in order to assess the model accuracy and robustness.
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