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.
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Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted.
The model fitting can include both variable selection and parameter estimation.
a neural net or a naive Bayes classifier) is trained on the training dataset using a supervised learning method (e.g. In practice, the training dataset often consist of pairs of an input vector and the corresponding answer vector or scalar, which is commonly denoted as the target.
The current model is run with the training dataset and produces a result, which is then compared with the target, for each input vector in the training dataset.
A better fitting of the training dataset as opposed to the test dataset usually points to overfitting.