A processor obtains a first measurement value representing prediction performance of a model that has been learned by using a first parameter value and training data of first size. The processor calculates a first expected value and a first variance degree of prediction performance of a model that would be learned by using the first parameter value and training data of second size. The processor also obtains a second measurement value representing prediction performance of a model that has been learned by using a second parameter value and training data of the first size. The processor calculates a second expected value and a second variance degree of prediction performance of a model that would be learned by using the second parameter value and training data of the second size.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable medium storing a program that causes a computer to perform a procedure comprising: obtaining a first measurement value representing prediction performance of a model that has been learned by using a first parameter value and training data of first size, the first parameter value being among a set of parameter values applicable to a machine learning algorithm; calculating, based on the first measurement value, a first expected value and a first variance degree of prediction performance of a model that would be learned by using the first parameter value and training data of second size; obtaining a second measurement value representing prediction performance of a model that has been learned by using a second parameter value and training data of the first size, the second parameter value being among the set of parameter values; calculating, based on the second measurement value, a second expected value and a second variance degree of prediction performance of a model that would be learned by using the second parameter value and training data of the second size; and calculating, based on the first and second expected values and the first and second variance degrees, a third expected value and a third variance degree of prediction performance of a model that would be learned by using a third parameter value and training data of the second size, the third parameter value being among the set of parameter values. 2. The non-transitory computer-readable storage medium according to claim 1, wherein the third variance degree is greater than a variance degree that is obtained through a regression analysis using the first and second expected values, but using neither the first variance degree nor the second variance degree. 3. The non-transitory computer-readable storage medium according to claim 1, wherein the calculating of a third variance degree includes: extracting first sample values of prediction performance, based on the first expected value and the first variance degree; extracting second sample values of prediction performance, based on the second expected value and the second variance degree; and calculating a third variance degree by using the first sample values and the second sample values. 4. The non-transitory computer-readable storage medium according to claim 1, wherein the calculating of a third variance degree includes: calculating a first variance component indicating a variation of the third expected value that is caused by variations of the first expected value and the second expected value; calculating a second variance component indicating statistical error that is caused by estimating the third expected value from the first expected value and the second expected value; and calculating a third variance degree by consolidating the first variance component and the second variance component. 5. The non-transitory computer-readable storage medium according to claim 1, wherein the procedure further comprises: calculating a reference value of prediction performance greater than the third expected value, based on the third expected value and the third variance degree; and determining, based on the reference value, whether to execute machine learning using the third parameter value and training data of the second size. 6. A method for managing a machine learning process, comprising: obtaining, by a processor, a first measurement value representing prediction performance of a model that has been learned by using a first parameter value and training data of first size, the first parameter value being among a set of parameter values applicable to a machine learning algorithm; calculating, by the processor and based on the first measurement value, a first expected value and a first variance degree of prediction performance of a model that would be learned by using the first parameter value and training data of second size; obtaining, by the processor, a second measurement value representing prediction performance of a model that has been learned by using a second parameter value and training data of the first size, the second parameter value being among the set of parameter values; calculating, by the processor and based on the second measurement value, a second expected value and a second variance degree of prediction performance of a model that would be learned by using the second parameter value and training data of the second size; and calculating, by the processor and based on the first and second expected values and the first and second variance degrees, a third expected value and a third variance degree of prediction performance of a model that would be learned by using a third parameter value and training data of the second size, the third parameter value being among the set of parameter values. 7. An apparatus that manages a machine learning process, the apparatus comprising: a memory configured to store therein a first measurement value and a second measurement value, the first measurement value representing prediction performance of a model that has been learned by using a first parameter value and training data of first size, the first parameter value being among a set of parameter values applicable to a machine learning algorithm, the second measurement value representing prediction performance of a model that has been learned by using a second parameter value and training data of the first size, the second parameter value being among the set of parameter values; and a processor, coupled to the memory, configured to perform a procedure including: calculating, based on the first measurement value, a first expected value and a first variance degree of prediction performance of a model that would be learned by using the first parameter value and training data of second size; calculating, based on the second measurement value, a second expected value and a second variance degree of prediction performance of a model that would be learned by using the second parameter value and training data of the second size; and calculating, based on the first and second expected values and the first and second variance degrees, a third expected value and a third variance degree of prediction performance of a model that would be learned by using a third parameter value and training data of the second size, the third parameter value being among the set of parameter values.