US 12,755,200 B2Granted

Method and system for interpreting machine learning model's prediction

AssigneeTata Consultancy Services Limited

InventorsMustafa Joherbhai Fatakdawala, Kavita Suresh Parab

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Abstract

This disclosure relates to field of machine learning. The outcome of the ML model translates to economic damage for a business. While the risk associated with outcome of ML model can be mitigated by interpreting/explanation each prediction of the ML model, interpreting ML models is challenging as ML models are built in dynamically changing complex mathematical design. The disclosure is a technique for interpreting machine learning model's prediction by computing a percentage contribution of each of the predictors. The percentage contribution indicates an importance of each of the predictors during prediction by the ML model. The percentage contribution is computed in several steps using several parameters associated with the ML Model including a pre-defined threshold of the ML model, an input feature vector comprising a plurality of predictors, an original prediction (N) for each predictor, a pre-determined duplication factor, and a plurality of pre-trained data statistics.

Claims9

Dependent claims

A processor-implemented method for interpreting machine learning model's prediction comprising:

receiving a plurality of input data parameters associated with a Machine Learning (ML) model, via one or more hardware processors, wherein the plurality of input data parameters includes a pre-defined threshold of the ML model, an input feature vector comprising a plurality of predictors, an original prediction (N) for each predictor from the plurality of predictors, a pre-determined duplication factor, and a plurality of pre-trained data statistics;
creating a plurality of duplicate data set of the input feature vector, via the one or more hardware processors, wherein the plurality of duplicate data set is created using the plurality of predictors based on the pre-determined duplication factor;
computing a contribution factor for each predictor in the duplicate data set, via the one or more hardware processors, wherein the process of computing the contribution factor for each predictor in the duplicate data set comprises:
replacing the predictor in each duplicate data set in the plurality of duplicate data set with a set of random values to obtain the plurality of estimator data set using the plurality of pre-trained data statistics;
obtaining a prediction probability for each of the duplicate data sets using the ML model;
predicting a final prediction (FP) for each of the duplicate data sets based on the prediction probability and the pre-defined threshold of the ML model, wherein the final prediction (FP) comprises one of a first pre-defined value and a second pre-defined value, wherein the final prediction is predicted as the first pre-defined value for prediction probability equal or higher than the pre-defined threshold and the final predication is predicted as the second pre-defined value for prediction probability lower than the pre-defined threshold;
and
computing the contribution factor (CF) for the predictor using the final predictions of the duplicate data set and the original prediction of the predictor;
and
interpreting the ML model by computing a percentage contribution of each of the predictors, via the one or more hardware processors, using the contribution factor of the plurality of predictors, wherein the percentage contribution indicates an importance of each of the predictors during prediction by the ML model.

7 more elements

A system comprising:

a memory storing instructions;
one or more communication interfaces;
and
one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
receive a plurality of input data parameters associated with a Machine Learning (ML) model, via one or more hardware processors, wherein the plurality of input data parameters includes a pre-defined threshold of the ML model, an input feature vector comprising a plurality of predictors, an original prediction for each predictor from the plurality of predictors, a pre-determined duplication factor, and a plurality of pre-trained data statistics;
create a plurality of duplicate data set of the input feature vector, via the one or more hardware processors, wherein the plurality of duplicate data set is created using the plurality of predictors based on the pre-determined duplication factor;
compute a contribution factor for each predictor in the duplicate data set, via the one or more hardware processors, wherein the process of computing the contribution factor for each predictor in the duplicate data set comprises:
replacing the predictor in each duplicate data set in the plurality of duplicate data set with a set of random values to obtain the plurality of estimator data set using the plurality of pre-trained data statistics;
obtaining a prediction probability for each of the duplicate data sets using the ML model;
predicting a final prediction for each of the duplicate data sets based on the prediction probability and the pre-defined threshold of the ML model, wherein the one or more hardware processors are configured by the instructions to estimate the final prediction, wherein the final prediction comprises one of a first pre-defined value and a second pre-defined value, wherein the final prediction is predicted as the first pre-defined value for prediction probability equal or higher than the pre-defined threshold and the final predication is predicted as the second pre-defined value for prediction probability lower than the pre-defined threshold;
and
computing the contribution factor for the predictor using the final predictions of the duplicate data set and the original prediction of the predictor;
and
interpret the ML model by computing a percentage contribution of each of the predictors, via the one or more hardware processors, using the contribution factor of the plurality of predictors, wherein the percentage contribution indicates an importance of each of the predictors during prediction by the ML model.

11 more elements

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

receiving a plurality of input data parameters associated with a Machine Learning (ML) model, via one or more hardware processors, wherein the plurality of input data parameters includes a pre-defined threshold of the ML model, an input feature vector comprising a plurality of predictors, an original prediction for each predictor from the plurality of predictors, a pre-determined duplication factor, and a plurality of pre-trained data statistics;
creating a plurality of duplicate data set of the input feature vector, via the one or more hardware processors, wherein the plurality of duplicate data set is created using the plurality of predictors based on the pre-determined duplication factor;
computing a contribution factor for each predictor in the duplicate data set, via the one or more hardware processors, wherein the process of computing the contribution factor for each predictor in the duplicate data set comprises:
replacing the predictor in each duplicate data set in the plurality of duplicate data set with a set of random values to obtain the plurality of estimator data set using the plurality of pre-trained data statistics;
obtaining a prediction probability for each of the duplicate data sets using the ML model;
predicting a final prediction for each of the duplicate data sets based on the prediction probability and the pre-defined threshold of the ML model, wherein the final prediction (FP) comprises one of a first pre-defined value and a second pre-defined value, wherein the final prediction is predicted as the first pre-defined value for prediction probability equal or higher than the pre-defined threshold and the final predication is predicted as the second pre-defined value for prediction probability lower than the pre-defined threshold;
and
computing the contribution factor for the predictor using the final predictions of the duplicate data set and the original prediction of the predictor;
and
interpreting the ML model by computing a percentage contribution of each of the predictors, via the one or more hardware processors, using the contribution factor of the plurality of predictors, wherein the percentage contribution indicates an importance of each of the predictors during prediction by the ML model.

7 more elements

Dependencies are inferred from the claim text.