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Firstly it is important to note how misleading it is to use the training set to define the AD, as commonly done by QSAR practitioners.
However, unfortunately, we use the training set in the ICDAR 2015 dataset in the evaluation, since the test set is not available.
In a real-world application one would have to use the training set for selecting hyperparameters to use in unknown data.
Third, we use the training set to obtain two Support Vector Machine (SVM) models, resulting in two feature-based interface similarity measures.
We use the training set to learn the parameters of the model and use the test set to evaluate the goodness of the model.
Participants can use the training set, open literature information and any other publicly available data.
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tPERSEVERE was redesigned using the training set and CART methodology.
Congeners of this set are well suited for validation of QSAR models developed using the training set.
The multivariate calibrations were obtained by measuring the zero-order absorbance from 220 to 320 nm using the training set.
It then predicts a resistant phenotype for samples containing resistant alleles of sufficiently high frequency, where 'sufficiently high' is estimated using the training set.
The multivarate calibrations were obtained by measuring the zero-order and first-derivative absorbances at 15 points from 220 to 290 nm using the training set.
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