1. A method for carrying out predictive analysis, comprising: receiving, using a processor of a computer system, a base model (M gen ) estimating a base model distribution (P gen (Y|X)) based on an original training set (D orig ) containing tuples (x,y) of indicators (x) and corresponding labels (y); receiving a distribution (P true (X)) approximating a true distribution of indicators; generating a random data set (D′) of indicators (x) based on the true distribution of indicators (P true (X)); applying the base model (P gen (Y|X)) to the random data set (D′) of indicators to assign a label (y) or a distribution of labels to each indicator (x) in the random data set (D′) of indicators and to generate an adjusted training set (D adj ); and training an adjusted predictive model (P adj (Y|X)) based on the adjusted training set (D adj ).