Steps in validating a test
Both fitted models are plotted with both the training and test sets.In the training set, the MSE of the fit shown in orange is 4 whereas the MSE for the fit shown in green is 9.Depending on the application, it can be derived from the confusion matrix and, uncovering the reasons for typical errors and finding ways to prevent the system make those in the future.For example, on the validation set one can see which classes are most frequently mutually confused by the system and then the instance space decomposition is done as follows: firstly, the classification is done among well recognizable classes, and the difficult to separate classes are treated as a single joint class, and finally, as a second classification step the joint class is classified into the two initially mutually confused classes.If a model fit to the training dataset also fits the test dataset well, minimal overfitting has taken place (see figure below).A better fitting of the training dataset as opposed to the test dataset usually points to overfitting.
The model is initially fit on a training dataset, The model (e.g.These repeated partitions can be done in various ways, such as dividing into 2 equal datasets and using them as training/validation, and then validation/training, or repeatedly selecting a random subset as a validation dataset.To validate the model performance, sometimes an additional test dataset that was held out from cross-validation is used.A test set is therefore a set of examples used only to assess the performance (i.e. A training set (left) and a test set (right) from the same statistical population are shown as blue points.
Two predictive models are fit to the training data.
It serves for learning more accurate concepts due to simpler classification boundaries in subtasks and individual feature selection procedures for subtasks.