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- W2488284206 abstract "The process of challenging a discriminant to assess its performance on future samples is called validation. There are two issues of major importance that need to be addressed during a validation study. One is the error rate of the classifier, which can be computed directly from the training set data using cross-validation or bootstrapping. Alternatively, it can be determined from samples of known class membership that are not part of the original training set. The second issue is related to so-called batch effects. The classification of samples on the basis of their chromatographic or spectroscopic profiles is often complicated by the confounding of the desired chemical information by experimental variables or other artifacts in the data. If the basis of classification for samples in the training set is other than the desired chemical differences characteristic of the source profiles of the respective classes in the data set, unfavorable classification results for the prediction (independent test) set will be obtained despite a linearly separable training set. The existence of these types of complicating relationships is an inherent part of ‘fingerprint-type’ data. Two studies involving these effects and methods for dealing with them are discussed." @default.
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- W2488284206 date "2009-01-01" @default.
- W2488284206 modified "2023-10-18" @default.
- W2488284206 title "Validation of Classifiers" @default.
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- W2488284206 doi "https://doi.org/10.1016/b978-044452701-1.00027-2" @default.
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