obviouslyusing fft points to the right direction (the classical genre was not that bad)but it is not enough to get decent classifier surelywe can play with the number of fft components (fixed to , but before we dive into parameter tuningwe should do our research there we find that fft is indeed not bad feature for genre classification--it is just not refined enough shortlywe will see how we can boost our classification performance by using processed version of it before we do thathoweverwe will learn another method of measuring classification performance an alternate way to measure classifier performance using receiver operator characteristic (rocwe have already learned that measuring accuracy is not enough to truly evaluate classifier insteadwe relied on precision-recall curves to get deeper understanding of how our classifiers perform there is sister of precision-recall curvescalled receiver operator characteristic (rocthat measures similar aspects of the classifier' performancebut provides another view on the classification performance the key difference is that / curves are more suitable for tasks where the positive class is much more interesting than the negative oneor where the number of positive examples is much less than the number of negative ones information retrieval or fraud detection are typical application areas on the other handroc curves provide better picture on how well the classifier behaves in general to better understand the differenceslet us consider the performance of the trained classifier described earlier in classifying country songs correctly