Model stability detection method, apparatus, and device

An electronic device, comprising:

• one or more processors;
• and
• a memory having one or more programs stored thereon,
• wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a model stability detecting method comprising:
• obtaining base period labeled samples and base period unlabeled samples at a base period time point, and current unlabeled samples at a current period time point, the base period time point preceding the current period time point;
• training a decision tree model based on the base period labeled samples, to obtain a base period decision tree model, the base period decision tree model comprising at least one path from a root node to a leaf node;
• applying the base period decision tree model to predict the base period unlabeled samples, obtaining a first sample statistic corresponding to each of the at least one path after the predicting, and forming a first set of numerical values by first sample statistics corresponding to respective paths in the at least one path;
• applying the base period decision tree model to predict the current period unlabeled samples, obtaining a second sample statistic corresponding to each of the at least one path after the predicting, and forming a second set of numerical values by second sample statistics corresponding to respective paths in the at least one path, the first sample statistic and the second sample statistic being of a same type;
• and
• obtaining a first distribution difference result of the first set of numerical values and the second set of numerical values, and determining, in response to the first distribution difference result meeting a first preset condition, that data drift has occurred.

A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a model stability detecting method comprising:

• obtaining base period labeled samples and base period unlabeled samples at a base period time point, and current unlabeled samples at a current period time point, the base period time point preceding the current period time point;
• training a decision tree model based on the base period labeled samples, to obtain a base period decision tree model, the base period decision tree model comprising at least one path from a root node to a leaf node;
• applying the base period decision tree model to predict the base period unlabeled samples, obtaining a first sample statistic corresponding to each of the at least one path after the predicting, and forming a first set of numerical values by first sample statistics corresponding to respective paths in the at least one path;
• applying the base period decision tree model to predict the current period unlabeled samples, obtaining a second sample statistic corresponding to each of the at least one path after the predicting, and forming a second set of numerical values by second sample statistics corresponding to respective paths in the at least one path, the first sample statistic and the second sample statistic being of a same type;
• and
• obtaining a first distribution difference result of the first set of numerical values and the second set of numerical values, and determining, in response to the first distribution difference result meeting a first preset condition, that data drift has occurred.