Sentence examples for validation training from inspiring English sources

Thus we got 20 networks after the twice 10-fold cross validation training and validating each time and these 20 networks were combined to generate the ensemble models by simple average of the outputs.

Also, it will be useful for supporting the local scientific community on development of products and process using gamma radiation, assisting the traditional and potential users on process validation, training and qualification of operators and radioprotection officers.

Table 1 Statistical performance of models using 8 MDL descriptors (Model A) and 12 Dragon descriptors (Model B).   Model A (8 MDL descriptors) Model B (12 Dragon descriptors) Internal validation Training (644 compounds) Test (161 compounds) Training (644 compounds) Test (161 compounds) Accuracy, % 91 73896969 Sensitivity, % 96759075 75 Specificity, % 86698761 61.

We also present the pseudo code for the meta-learning training algorithm for the jth cross validation training process in Algorithm 2. The in-memory meta-learning presented in section Meta-learning Algorithm and the distributed meta-learning which we are going to provide in this section differ as follows.

Cross validation training and test sets were the same for all evaluated models, which allowed for a direct comparison between models.

We applied 10-fold cross validation training-testing strategy in the classification experiments.

The cross-validation training was as follows.

We evaluate the quality of the feature selection through 10-fold-cross-validation, training the models with the reduced set of features on the training set.

However, a sixth-order polynomial did yield a marginal result (t[93] = 3.17, p = .002, adjusted R 2 = .17), although it failed cross-validation (training set adjusted R 2 = .15, test set R 2 < 0 .8 The point here is that buried within the essentially random data is a suspicion of a pattern that bears some resemblance to the middle panel of Fig. 1.

The pooled datasets of positive training datasets and negative training datasets were randomly divided into five subsets with approximately equal number for cross-validation training.

Feature selection was done as a preprocessing step in each of the cross-validation training folds of the data sets.