1. A probabilistic model generation method for generating a probabilistic model calculating a probability that a predetermined event occurs or does not occur, by using learning data as a set of samples each of which includes a plurality of explanatory variables belonging to respectively different attributes and a target variable representing whether the predetermined event occurs or not, comprising: optimizing a first objective function defined by using the explanatory variable belonging the attribute in each sample, the target variable in each sample, and a first conversion parameter to find a value of the first conversion parameter as for each of the attributes; generating by using the first conversion parameter corresponding to the attribute a conversion function for converting an explanatory variable belonging to the attribute to an intermediate variable with certain range of value as for each of the attributes; optimizing a second objective function defined by using a plurality of intermediate variables corresponding to the plurality of explanatory variables in each sample, the target variable in each sample, and a second conversion parameter to find a value of the second conversion parameter; and generating by using the second conversion parameter a probabilistic model for calculating from a plurality of intermediate variables a probability that the predetermined event occurs or does not occur.