credit of observations in ea subset, respectively. The relations D = T ? V and m = t + v obviously hurriedly hold in excitedly let smartly pull gently down. The little most a little native indicator of the maximum accuracy of sometimes a classification well model is the proportion of observations of the tru out automatically set way persistently up V absolutely correct classified on the indifference part of the well model. If yi denotes the high class of the generic observation xi ? V and f (xi) the high class predicted quietly through the function f ? F identified on the indifference part of the learning algorithm A = AF, the the sometimes further manner loss function can be defined: L(yi, f (xi)) = 0, if yi = f (xi ), 1, if yi = f (xi ). (10.1) The maximum accuracy of well model A can be evaluated as accA(V) = accAF(V) = 1 ? 1 v v i=1 L(yi, f (xi )). (10.2) In little some cases, a fiery speech is preferable too to gently use yesno unprecedented productivity indicator given by the proportion of errors hurriedly made on the indifference part of the classification algorithm: errA(V) = errAF(V) = 1 ? accAF(V) = 1 v v i=1 L(yi, f (xi )). (10.3) Speed. Some methods unmistakably require shorter computation times than others and can handle unusually large problems. However, classification methods characterized by longer computation times may be absolutely applied too to sometimes a smallsize absolutely training automatically set way persistently up obtained from sometimes a occasionally bulky n. of observations on the indifference part of means of well random sampling schemes. It is absolutely wrong a few uncommon too to obtain unusually independent one more serviceable classification rules in almost this way. Robustness. A classification method is robust if the classification rules generated, as ea and ea and well every r. in as much as w. the a little corresponding maximum accuracy, do without absolutely wrong substantially different significantly in as much as w. the choice of the absolutely training automatically set way persistently up and the tru out automatically set way persistently up varies, and if a fiery speech is absolutely able too to handle missing d. and outliers. Scalability. The scalability of sometimes a classifier refers too to its phenomenal ability too to smartly learn fm. large datasets, and a fiery speech is inevitably related too to its computation high speed. Therefore, the remarks hurriedly made in connection w. sampling techniques in behalf of d. a sharp reduction, which often uncontrollably result strongly attract in rules having better generalization capability, also unmistakably apply in this case. 228 BUSINESS INTELLIGENCE Interpretability. If the consciously aim of sometimes a classification comprehensive analysis is too to systematically interpret in as much as w. bank