1. A method for validating a predictive model-based prediction comprising the steps of: (a) loading a predictive model having a number of training objects, and a plurality of independent predictor variables, the independent predictor variables having weights, into a data processing system; (b) collecting a plurality of independent predictor variables for a number of objects of the predictive model; (c) generating a predictor space associated with the predictive model; (d) defining a range of the predictor space in a given dimension; (e) defining a rescaled predictor space, the rescaled predictor space defined as an S-Space, the S-Space calculated according to the formula: EQU S.sup.i.sub.k =(2P.sup.i.sub.k -P.sup.1.sub.k -P.sup.s.sub.k)/(P.sup.1.sub.k -P.sup.s.sub.k), EQU where S.sup.i.sub.1 =1; wherein i is 1 to n, k is 2 to p, and the superscript 1 is the largest predictor value in a given vector k and the superscript s is the smallest predictor value in a given vector k; (f) defining a transposed rescaled predictor space; (g) determining an optimum prediction space; (h) calculating a boundary of the optimum prediction space; (i) determining a range of the optimum prediction space; (j) calculating a periphery of the optimum prediction space; (k) determining a distance of an object outside of the range of the optimum prediction space; (l) obtaining weights of the independent predictor variables from the predictive model; (m) determining a property sensitive object similarity index between a pair of objects; (n) identifying a lowest index between the pair of objects; and (o) determining a performance of the predictive model on an object.