In an approach for evaluating a predictive model, a computer identifies features of training samples in a set of training samples and selects at least one evaluation metric from a set of evaluation metrics as one or more available metrics based on the identified features. The computer applies a predictive model created based on the set of training samples to a set of test samples so as to calculate values of the one or more available metrics and evaluates the predictive model by using the one or more available metrics and the values of the available metrics. With the technical solutions described with respect to the embodiments of the present invention, one or more evaluation metrics that are applicable to specific training sample features may be determined from several evaluation metrics, so that users can precisely evaluate predictive models by using the determined evaluation metrics.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for evaluating a predictive model, comprising: identifying, by one or more computer processors, features of training samples in a set of training samples; selecting, by one or more computer processors, at least one evaluation metric from a set of evaluation metrics as one or more available metrics based on the identified features; applying, by one or more computer processors, a predictive model created based on the set of training samples to a set of test samples so as to calculate values of the one or more available metrics; and evaluating, by one or more computer processors, the predictive model by using the one or more available metrics and the values of the one or more available metrics. 2. The method according to claim 1, wherein the set of evaluation metrics comprises a plurality of first elements, each of which comprises an evaluation metric and at least one attribute associated with the evaluation metric. 3. The method according to claim 2, wherein selecting, by one or more computer processors, at least one evaluation metric from a set of evaluation metrics as one or more available metrics based on the identified features comprises: comparing, by one or more computer processors, the identified features with the at least one attribute of each evaluation metric in the set of evaluation metrics; and in response to the identified features matching at least one attribute of at least one evaluation metric in the set of evaluation metrics, selecting, by one or more computer processors, the at least one evaluation metric as the one or more available metrics. 4. The method according to claim 2, wherein the at least one attribute associated with the evaluation metric at least comprises a type of samples to which the evaluation metric is applicable, and a type of a data mining task to which the evaluation metric is applicable. 5. The method according to claim 1, wherein the identified features at least comprise a type of the training samples and a type of a data mining task to which the training samples are directed. 6. The method according to claim 1, wherein selecting, by one or more computer processors, at least one evaluation metric from a set of evaluation metrics as one or more available metrics comprises: determining, by one or more computer processors, one or more recommended metrics based on the predictive model, the one or more available metrics and a predetermined first set of user-preferred metrics; wherein applying, by one or more computer processors, a predictive model created based on the set of training samples to a set of test samples so as to calculate values of the one or more available metrics comprises determining values of the one or more recommended metrics based on the values of the available metric; and wherein evaluating, by one or more computer processors, the predictive model comprises evaluating the predictive model by using the one or more recommended metrics and the values of the one or more recommended metrics. 7. The method according to claim 6, wherein the predetermined first set of user-preferred metrics comprises a plurality of second elements, each of which comprises a first user-preferred metric and at least one attribute associated with the first user-preferred metric, and wherein the at least one attribute associated with the first user-preferred metric at least comprises a weight indicating a degree of a user's preference to the first user-preferred metric. 8. The method according to claim 7, wherein determining, by one or more computer processors, one or more recommended metrics comprises: comparing, by one or more computer processors, the one or more available metrics with first user-preferred metrics in the predetermined first set of user-preferred metrics; selecting, by one or more computer processors, one or more first user-preferred metrics matching the one or more available metrics from the first set of user-preferred metrics; in response to a user's input indicating a desired number of the one or more recommended metrics, ranking, by one or more computer processors, the selected one or more first user-preferred metrics by weight; and sequentially selecting, by one or more computer processors, the desired number of the one or more first user-preferred metrics as the one or more recommended metrics from the ranked one or more first user-preferred metrics. 9. The method according to claim 6, wherein determining one or more recommended metrics further comprises: receiving, by one or more computer processors, a second set of user-preferred metrics from a user; comparing, by one or more computer processors, the one or more recommended metrics with second user-preferred metrics in the second set of user-preferred metrics; and selecting, by one or more computer processors, one or more second user-preferred metrics matching the one or more recommended metrics from the second set of user-preferred metrics; wherein determining values of the one or more recommended metrics comprises: determining, by one or more computer processors, the values of the one or more recommended metrics matching the selected second user-preferred metrics as values of the selected second user-preferred metrics, and wherein evaluating, by one or more computer processors, the predictive model comprises: evaluating, by one or more computer processors, the predictive model by using the selected second user-preferred metrics and the values of the selected second user-preferred metrics. 10. The method according to claim 9, wherein the second set of user-preferred metrics comprises a plurality of third elements, each of which at least comprises a second user-preferred metric and a weight associated with the second user-preferred metric. 11. The method according to claim 10, further comprising: updating, by one or more computer processors, the first set of user-preferred metrics by using the second user-preferred metrics and the weight of the second user-preferred metric. 12. An apparatus for evaluating a predictive model, the apparatus comprising: one or more computer processors; one or more computer readable storage media; program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: an identifying module configured to identify features of training samples in a set of training samples; a selecting module configured to select at least one evaluation metric from a set of evaluation metrics as one or more available metrics based on the identified features; a calculating module configured to apply a predictive model created based on the set of training samples to a set of test samples so as to calculate values of the one or more available metrics; and an evaluating module configured to evaluate the predictive model by using the one or more available metrics and the values of the one or more available metrics. 13. The apparatus according to claim 12, wherein the set of evaluation metrics comprises a plurality of first elements, each of which comprises an evaluation metric and at least one attribute associated with the evaluation metric. 14. The apparatus according to claim 13, wherein the selecting module comprises: a first comparing unit configured to compare the identified features with the at least one attribute of each evaluation metric in the set of evaluation metrics; and a first selecting unit configured to, in response to the identified features matching at least one attribute of at least one evaluation metric, select the at least one evaluation metric as the one or more available metrics. 15. The apparatus according to claim 13, wherein the at least one attribute associated with the evaluation metric at least comprises a type of samples to which the evaluation metric is applicable, and a type of a data mining task to which the evaluation metric is applicable. 16. The apparatus according to claim 12, wherein the identified features at least comprise a type of the training samples and a type of a data mining task to which the training samples are directed. 17. The apparatus according to claim 12, wherein the selecting module comprises a first determining module configured to determine one or more recommended metrics based on the predictive model, the one or more available metrics and a predetermined first set of user-preferred metrics; wherein the calculating module comprises a second determining module configured to determine values of the one or more recommended metrics based on the values of the available metric; and wherein the evaluating module is further configured to evaluate the predictive model by using the one or more recommended metrics and the values of the recommended metrics. 18. The apparatus according to claim 17, wherein the predetermined first set of user-preferred metrics comprises a plurality of second elements, each of which comprises a first user-preferred metric and at least one attribute associated with the first user-preferred metric, and wherein the at least one attribute associated with the first user-preferred metric at least comprises a weight indicating a degree of a user's preference to the first user-preferred metric. 19. The apparatus according to claim 18, wherein the first determining module comprises: a second comparing unit configured to compare the one or more available metrics with first user-preferred metrics in the first set of user-preferred metrics; a second selecting unit configured to select one or more first user-preferred metrics matching the one or more available metrics from the first set of user-preferred metrics; a first ranking unit configured to, in response to a user's input indicating a desired number of the one or more recommended metrics, rank the selected first user-preferred metrics by weight; and a sequentially selecting unit configured to sequentially select the desired number of the first user-preferred metrics as the one or more recommended metrics from the ranked first user-preferred metrics. 20. The apparatus according to claim 17, wherein the first determining module further comprises: a receiving module configured to receive a second set of user-preferred metrics from a user; a comparing module configured to compare the one or more recommended metrics with second user-preferred metrics in the second set of user-preferred metrics; and a user-preferred metric selecting module configured to select one or more second user-preferred metrics matching the one or more recommended metrics from the second set of user-preferred metrics; wherein the second determining module is further configured to determine the values of the recommended metrics matching the selected second user-preferred metrics as values of the selected second user-preferred metrics, and wherein the evaluating module is further configured to evaluate the predictive model by using the selected second user-preferred metrics and the values of the selected second user-preferred metrics.