Esemble of Classifiers (part I)

Ensemble of Classifiers is a powerful machine learning technique where several (weak) classifiers are combined to obtain a more powerful classifier.

In this code skeleton, a generic Classifier class is defined which takes a generic input T1 type as input and classifies (transforms) into a generic type T2 (the categories). The generic classifier is then specialized into a weak classifier class with T1 = vector [integer] and T2 = bool. The weak classifier has some random noise which is used to generate mistakes.

A collection of weak classifiers will be then combined into an ensembles of classifiers, in the next days.