Sentence examples for classification of the training from inspiring English sources

Table 5 classification results include the classification of the training sample and the classification of the test sample.

Increasing the value of C can always achieve the correct classification of the training samples, but this will lead to over-fitting and bad generalization performance.

The approach performs a trade-off between accuracy of classification of the training examples and ability to generalize; the price to pay is the introduction of a "regularization" constant C whose value must be chosen appropriately.

In oil production, this has two shortcomings: firstly, it is easily affected by the subjective experience of the technical staff; secondly, there are larger workloads in manual classification of the training samples.

While the accuracy of the classification of the training dataset is very well (approx. 84 % for decision tree), the accuracy for the validation dataset dropped below 50%% for some algorithms, which is worse than that for random classification.

The independent test dataset accuracy rate denotes the percentage of samples in the test dataset that have been correctly diagnosed using the molecular signatures identified from classification of the training dataset.

In an unsupervised training, the network learns to form their own classifications of the training data without external help.

Employing this parameter value for re-classification of the training data yielded an increased Matthews correlation of mcc = 0.82.

Furthermore, when the classifications of the training dataset were compared, all of the members of SLC39A6/GATA3 Group 1 were in BCMP11/ABCC11 Group 1, all of the members of SLC39A6/GATA3 Group 3 were in BCMP11/ABCC11 Group 3, and the majority of the members of SLC39A6/GATA3 Group 2 were in the BCMP11/ABCC11 Group 2 (Table 5 upper).

In the case of a nonlinear classification of samples, the training vectors x i are mapped into a higher (maybe infinite) dimensional space by the function φ, w is the vector of hyperplane coefficients (orientation), b is a bias term.

Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples.