Automatic runtime preemptive alert

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to:

• select a plurality of sets of candidate factors, the candidate factors being indicators of computer performance, and wherein each set in the plurality of sets of candidate factors has a combination of candidate factors that is different from another set in the plurality of sets of candidate factors;
• for a set in the plurality of sets of candidate factors,
• train a classification model to predict a problem possibility during a computer runtime using a training dataset associated with the set,
• validate the classification model using a validation dataset associated with the set and determine an accuracy score associated with the classification model, and
• adjust factor weights associated with the candidate factors in the set based on the accuracy score,
• wherein the device is caused to repeat the training of the classification model, the validation of the classification model, and the adjusting of the factor weights for all sets in the plurality of sets of candidate factors, wherein a plurality of classification models is trained corresponding to the respective plurality of sets of candidate factors;
• select from the plurality of classification models a low accuracy classification model having an accuracy score lower than a threshold criterion;
• select from the plurality of classification models a higher accuracy classification model having an accuracy score that is higher than the accuracy score of the low accuracy classification model;
• update the set of candidate factors used to train the low accuracy classification model by replacing at least one candidate factor in the set of candidate factors used to train the low accuracy classification model with a candidate factor in the set of candidate factors used to train the higher accuracy classification model;
• and
• update the low accuracy classification model by repeating the training of the classification model, the validating of the classification model, and the adjusting of the factor weights, using the updated set of candidate factors.

A system comprising:

• at least one computer processor;
• at least one memory device coupled with the computer processor;
• the at least one computer processor configured to at least:
• select a plurality of sets of candidate factors, the candidate factors being indicators of computer performance, and wherein each set in the plurality of sets of candidate factors has a combination of candidate factors that is different from another set in the plurality of sets of candidate factors;
• for a set in the plurality of sets of candidate factors,
• train a classification model to predict a problem possibility during a computer runtime using a training dataset associated with the set,
• validate the classification model using a validation dataset associated with the set and determine an accuracy score associated with the classification model, and
• adjust factor weights associated with the candidate factors in the set based on the accuracy score,
• wherein the computer processor is configured to repeat the training of the classification model, the validation of the classification model, and the adjusting of the factor weights for all sets in the plurality of sets of candidate factors, wherein a plurality of classification models is trained corresponding to the respective plurality of sets of candidate factors;
• select from the plurality of classification models a low accuracy classification model having an accuracy score lower than a threshold criterion;
• select from the plurality of classification models a higher accuracy classification model having an accuracy score that is higher than the accuracy score of the low accuracy classification model;
• update the set of candidate factors used to train the low accuracy classification model by replacing at least one candidate factor in the set of candidate factors used to train the low accuracy classification model with a candidate factor in the set of candidate factors used to train the higher accuracy classification model;
• and
• update the low accuracy classification model by repeating the training of the classification model, the validating of the classification model, and the adjusting of the factor weights, using the updated set of candidate factors.