Method, electronic device, and computer program product for detecting model drift

An electronic device, comprising:

• a processor;
• and
• a memory coupled to the processor, the memory having instructions stored therein, wherein the instructions, when executed by the processor, cause the device to perform actions comprising:
• converting training data in a training data set into an input vector represented by Shapley values, a plurality of dimensions of the input vector indicating a plurality of input features of a decision tree model, and the decision tree model having been trained for performing at least one of image classification, text classification, or data mining;
• clustering, on the basis of the input vector, the training data set, so as to obtain a plurality of data clusters;
• in response to receiving a first input, converting the first input into a first input vector represented by Shapley values;
• and
• detecting a drift degree of the decision tree model on the basis of the first input vector and the plurality of data clusters.

A computer program product tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein the machine-executable instructions, when executed by a machine, cause the machine to perform a method for detecting a model drift, the method comprising:

• converting training data in a training data set into an input vector represented by Shapley values, a plurality of dimensions of the input vector indicating a plurality of input features of a decision tree model, and the decision tree model having been trained for performing at least one of image classification, text classification, or data mining;
• clustering, on the basis of the input vector, the training data set, so as to obtain a plurality of data clusters;
• in response to receiving a first input, converting the first input into a first input vector represented by Shapley values;
• and
• detecting a drift degree of the decision tree model on the basis of the first input vector and the plurality of data clusters.