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Furthermore, we built a classification model using logistic regression to assess the performance of the purine pathway analytes in separating the groups in space.
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This paper attempts to build a classification model according to the research products created by those institutes and hence to design specific evaluation processes.
This paper tackles the sovereign credit rating classification problem within an ordinal classification perspective by employing a pairwise class distances projection to build a classification model based on standard regression techniques.
To predict tie persistence we build a classification model using simple logistic regression (LogR) models where the positive class is tie persistence, that is, that we observe at least a communication event in (Omega_{mathrm{after}}).
Effectively this procedure builds a classification model with 90% of the spectra and then tests the remaining 10% to assess classification accuracy.
A slightly more sophisticated category of multivariate analysis is supervised methods, whereby a calibration dataset of known identity is required to build a classification model.
PLS-DA is a supervised classification method in which a prior knowledge is required for a training dataset in order to build a classification model to test unknown samples.
Model robustness was tested by cross-validation (CV) with Venetian Blinds, which builds a classification model based on 90% of the data, tests the remaining 10% against that model, and repeats the process through ten iterations to assess its predictive power.
We create training instances using simulated RNA-seq fragments from annotated genes in Ensembl to build a classification model.
After that, the training set is fed into the decision tree (classifier) to build a classification model.
We used Random Forest (RF) to build a classification model and to identify masses with the most discriminating potential.
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