Evaluate the Training Set: is it necessary? A theorecal Presentation of Y_Measure : New Metric for Evaluating Training Sets for Supervised Classification

All previous work was aimed at evaluating classification algorithms or the entire classification process, it fits with the studied problem in question, but never the representativeness of the Training Set. The Training Set has a great influence on the accuracy of the classification process and the reliability of results and evaluations. We do not doubt about friability and utility of the various measures proposed and applied, we want by this article to reinforce them by another measure of representativeness of the Training Set to allow the expert to better apprehend the results and to optimize the reliability and robustness of the evaluation and in order to better identify gaps either originates from the algorithm or the Training Set. In this article, we will show the need of new Training Set quality evaluation metric. We based on the theoretical criteria for the generation of an optimal Training Set. "Divide for Reign".