Abstract: A descriptor generation unit 81 uses a first template prepared in advance to generate a feature descriptor, which generates a feature that may affect a prediction target from a first table including a variable of the prediction target and a second table. A feature generation unit 82 generates the feature by applying the feature descriptor to the first and second tables. A feature explanation generation unit 83 generates a feature explanation about the feature descriptor or the feature on the basis of a second template. An accepting unit 84 accepts values to be assigned to the first and second templates. The descriptor generation unit 81 generates the feature descriptor by assigning the accepted values to the first template, and the feature explanation generation unit 83 generates the feature explanation by assigning the values assigned to the first template to the second template.

Abstract: A table acquiring means 381 acquires a first table including prediction objects and first attributes, and a second table including second attributes. A receiving means 382 receives a similarity function and condition for similarity used to calculate the similarity between the first attribute and the second attribute. A feature generating means 383 generates feature candidates able to affect a prediction object using a combination condition for combining a record in the first table including the value of a first attribute satisfying the condition with a record in the second table including the value of a second attribute satisfying the similarity calculated with the value of the first attribute and the value of the second attribute using the similarity function, and using a reduction method for a plurality of records in the second table and a reduction condition represented by the column to be aggregated. A feature selecting means 384 selects an optimum feature for the prediction from the feature candidates.

Abstract: A table storage unit 81 stores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unit 82 creates a feature descriptor for generating a feature which is a variable that can influence the objective variable, from the first table and the second table. The descriptor creation unit 82 creates a plurality of feature descriptors, each by generating a combination of a mapping condition element indicating a mapping condition for rows in the first table and the second table and a reduction method element indicating a reduction method for reducing, for each objective variable, data of each column included in the second table.

Abstract: A descriptor generation unit 81 uses a first template prepared in advance to generate a feature descriptor, which generates a feature that may affect a prediction target from a first table including a variable of the prediction target and a second table. A feature generation unit 82 generates the feature by applying the feature descriptor to the first and second tables. A feature explanation generation unit 83 generates a feature explanation about the feature descriptor or the feature on the basis of a second template. An accepting unit 84 accepts values to be assigned to the first and second templates. The descriptor generation unit 81 generates the feature descriptor by assigning the accepted values to the first template, and the feature explanation generation unit 83 generates the feature explanation by assigning the values assigned to the first template to the second template.

Abstract: An accepting unit 71 accepts a feature descriptor, which generates a feature, i.e. a variable that may affect a prediction target, from a first table including a variable of the prediction target and a second table. An extraction unit 72 extracts, from the feature descriptor, table information indicating a name of the second table, joint information indicating key columns when joining the first table and the second table, and aggregation information indicating an aggregation operation to be performed on a plurality of rows in the second table and a column as a target of the aggregation operation. A feature explanation generation unit 73 assigns the extracted information to a feature explanation template to generate a feature explanation of the feature, which is obtained by applying the feature generator to the first table and the second table.