Cross Validation (Concurrency)
SynopsisThis Operator performs a cross validation to estimate the statistical performance of a learning model.
It is mainly used to estimate how accurately a model (learned by a particular learning Operator) will perform in practice.
The Cross Validation Operator is a nested Operator. It has two subprocesses: a Training subprocess and a Testing subprocess. The Training subprocess is used for training a model. The trained model is then applied in the Testing subprocess. The performance of the model is measured during the Testing phase.
The input ExampleSet is partitioned into k subsets of equal size. Of the k subsets, a single subset is retained as the test data set (i.e. input of the Testing subprocess). The remaining k - 1 subsets are used as training data set (i.e. input of the Training subprocess). The cross validation process is then repeated k times, with each of the k subsets used exactly once as the test data. The k results from the k iterations are averaged (or otherwise combined) to produce a single estimation. The value k can be adjusted using the number of folds parameter.
The evaluation of the performance of a model on independent test sets yields a good estimation of the performance on unseen data sets. It also shows if 'overfitting' occurs. This means that the model represents the testing data very well, but it does not generalize well for new data. Thus, the performance can be much worse on test data.
This Operator is similar to the Cross Validation Operator but only splits the data into one training and one test set. Hence it is similar to one iteration of the cross validation.