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After the model is built, its predictive accuracy is then measured in a separate subset of the data, called the test set, where the algorithm knows only the values of the predictor attributes (and not classes) for data instances.
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Table 1 reports the computational results for three different data instances.
The result is a decision function, which returns a positive value for a data instance inside the domain and a negative for those outside of it.
In particular, since PSSM has 20 scores for each sequence position, it gives rise to 220 inputs for a data instance with eleven residues.
Hence, we have five (different) data instances for each UserID.
The number of negative instances was set as tenfold with positive instances to make enough data instances for training.
The models were then used to score the remaining data instances for prediction.
A suitable plugin parser for each data instance is chosen in order to parse it.
For each data instance i, (c_{i}) is known precisely according to the experiment design.
For each data instance, the input vector contains 319 feature values, including 220 (20 × 11) PSSMs, 66 (6 × 11) OBVs and 33 (3 × 11) SS elements.
Because each residue was encoded with 20 PSSM scores and 3 biochemical features, the input vector contained 253 values for a data instance with eleven residues.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com