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The test is dual phased and consists of (i) a training phase using a first set of real-life data, during which the hypothesis is said to be true, and (ii) a validation phase during which a second set of real-life data is used to evaluate whether the hypothesis holds true or not.
This dataset is divided into two parts: (i) A training set consisting of 123 oligomers for obtaining the best fit equation giving the minimum possible error and (ii) a test dataset consisting of 225 oligomers, to assess the quality of prediction on independent data.
Assessment of the interpolation power with a 10-fold CV (10-fold CV PGM) model: a given dataset was randomly divided into (i) a training set comprising 90% of the data points and (ii) a test set comprising the remaining 10% of the data (Fig. 1 b).
This approach consists of splitting the available data into i) a training set which is used to construct classifiers and select the "best" one and ii) a validation data, which is not opened until a final classifier has been selected based on the training data, in the vein of the validation strategy described by Daumer et al [ 34].
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The output of the learning machine is represented by F z), and the desired output by t H i. A training set, =0∪1, where 1 is composed of N 1 training patterns from hypothesis H 1, and 0 is composed of N 0 training patterns from hypothesis H 0 (N = N 1 + N 0), is available.
So I became a training consultant.
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"I'm a trained scientist; I've done the research.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com