Validating in algorithm
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), which splits a complete multi-class problem into a set of smaller classification problems.It serves for learning more accurate concepts due to simpler classification boundaries in subtasks and individual feature selection procedures for subtasks.The performance of the networks is then compared by evaluating the error function using an independent validation set, and the network having the smallest error with respect to the validation set is selected. Since this procedure can itself lead to some overfitting to the validation set, the performance of the selected network should be confirmed by measuring its performance on a third independent set of data called a test set.
The data used to build the final model usually comes from multiple datasets.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.When doing classification decomposition, the central choice is the order of combination of smaller classification steps, called the classification path.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.