1. A method for carrying out predictive analysis, comprising: receiving, using a processor of a computer system, a base model (Mgen) estimating a base model distribution (Pgen(Y|X)) based on an original training set (Dorig) containing tuples (x,y) of indicators (x) and corresponding labels (y), wherein the indicators (x) describe influence factors, and wherein the corresponding labels (y) describe a prediction; receiving a distribution (Ptrue(X)) approximating a true distribution of the indicators (x) that contains at least one assumption about reality; generating a random data set (D′) of the indicators (x) based on the true distribution of indicators (Ptrue(X)); applying the base model (Pgen(Y|X)) to the random data set (D′) of the indicators (x) to assign one of a label (y) and a distribution of labels to each indicator (x) in the random data set (D′) of the indicators (x) and to generate an adjusted training set (Dadj); and training an adjusted predictive model (Padj(Y|X)) based on the adjusted training set (Dadj), wherein the predictive model (Padj(Y|X)) represents the base model distribution (Pgen(Y|X)) and the distribution (Ptrue(X)).