Predicting method, predicting apparatus, and computer-readable recording medium

A predicting method comprising:

• receiving input data to be subjected to prediction;
• and
• generating, from training data sets each having explanatory variables and an objective variable, a prediction result using the input data, by using a hypothesis set and respective weights of a plurality of hypotheses included in the hypothesis set, the hypothesis set including the hypotheses each being formed of a combination of the explanatory variables, classifying any of the training data sets and satisfying a specific condition, the weights being obtained by machine learning based on whether or not each of the hypotheses included in the hypothesis set holds true for each of the training data sets, by a processor, wherein
• the generating includes determining a value of a variable included in a pseudo-Boolean function such that a probability satisfies a predetermined standard, the probability being a probability that the prediction result using the input data satisfies the specific condition, the probability being calculated by the pseudo-Boolean function generated as a result of the machine learning, the pseudo-Boolean function including variables corresponding to the explanatory variables and used in calculation of a probability that the specific condition is satisfied.

A predicting apparatus comprising:

• an input unit that receives input data to be subjected to prediction;
• and
• a processor that is configured to generate, from training data sets each having explanatory variables and an objective variable, a prediction result using the input data, by using a hypothesis set and respective weights of a plurality of hypotheses included in the hypothesis set, the hypothesis set including the hypotheses each being formed of a combination of the explanatory variables, classifying any of the training data sets and satisfying a specific condition, the weights being obtained by machine learning based on whether or not each of the hypotheses included in the hypothesis set holds true for each of the training data sets, wherein
• the processor determines a value of a variable included in a pseudo-Boolean function such that a probability satisfies a predetermined standard, the probability being a probability that the prediction result using the input data satisfies the specific condition, the probability being calculated by the pseudo-Boolean function generated as a result of the machine learning, the pseudo-Boolean function including variables corresponding to the explanatory variables and used in calculation of a probability that the specific condition is satisfied.