This study evaluated the influence of the choice of the training subset in the construction of predictive models, as well as on their validation
2021
Kennard-Stone method outperforms the Random Sampling in the selection of calibration samples in SNPs and NIR data
Key concepts
Scholarcy highlights
- Under the framework of prediction modeling, splitting the data set into parts that will be used to build the models, as well as their validation, is a crucial step in their development
- near-infrared spectroscopy data dataset The original NIR spectra of the 256 sugarcane leave samples are shown in figure 2
- For the analysis and modeling of NIR spectroscopy data, we should apply some pretreatments on the data matrix as a way to remove or minimize the sources of spectral variability and to improve selectively
- It was observed that the values from the KS partition method were, in general, better than those obtained from Random Sampling within all repetitions
- When using the SNPs data set, we verified that there is a significant difference between the root of the mean squared error means obtained by the two methods, at the level of 1% significance, which indicates the superiority of the KS approach aiming to split the data set with the purpose of prediction
- Set in training and testing subsamples allow for predicting models that are statistically different
- The KS method may be a good alternative to commonly used partition methods for SNP data
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