Abstract: For each of one or a plurality of types of samples, a database is constructed in which a sample identification tag and data belonging to at least two types of categories out of Categories (1), (2), and (3) are stored in association with each other. Data to be used as training data in supervised learning is selected from the database. At this time, one or a plurality of types of data is selected as an explanatory variable from one of the categories. Further, one or a plurality of types of data is selected as an objective variable from the other category. Then, training data is generated in which data corresponding to the selected explanatory variable is served as input and data corresponding to the selected objective variable is served as ground truth output.