TECHNIQUES FOR VALIDATING FEATURES FOR MACHINE LEARNING MODELS

1 . A method for machine learning features validation, comprising:

performing statistical testing on a plurality of pairs of features, each pair of features including a test feature of a plurality of test features extracted from a first data set and a corresponding training feature extracted from a second data set during a training phase for a machine learning model, wherein the statistical testing is performed under a null hypothesis that the first data set and the second data set are drawn from a same continuous distribution, wherein performing the statistical testing further comprises determining a degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature; and

determining, based on the degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature, whether the plurality of test features is validated.

2 . The method of claim 1 , further comprising:

applying the machine learning model to the plurality of test features when the plurality of test features is validated; and

avoiding applying the machine learning model to the plurality of test features when the plurality of test features is not validated.

3 . The method of claim 1 , wherein determining a degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature further comprises:

determining a distance between a first distribution function of the first data set and a second distribution function of the second data set.

4 . The method of claim 3 , wherein the plurality of test features is determined as validated when the distance between the first distribution function of the first data set and the second distribution function of the second data set is below a threshold.

5 . The method of claim 3 , wherein the distance is determined using a Kolmogorov-Smirnov test.

6 . The method of claim 1 , wherein performing the statistical testing further comprises:

fitting a vector extracted from the first data set into a density estimator;

fitting a vector extracted from the second data set into the density estimator; and

drawing a sample from the density estimator, wherein the statistical testing is performed on the sample.

7 . The method of claim 6 , wherein the density estimator is a Gaussian density estimator.

8 . A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

performing statistical testing on a plurality of pairs of features, each pair of features including a test feature of a plurality of test features extracted from a first data set and a corresponding training feature extracted from a second data set during a training phase for a machine learning model, wherein the statistical testing is performed under a null hypothesis that the first data set and the second data set are drawn from a same continuous distribution, wherein performing the statistical testing further comprises determining a degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature; and

determining, based on the degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature, whether the plurality of test features is validated.

9 . A system for machine learning features validation, comprising:

a processing circuitry; and

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

perform statistical testing on a plurality of pairs of features, each pair of features including a test feature of a plurality of test features extracted from a first data set and a corresponding training feature extracted from a second data set during a training phase for a machine learning model, wherein the statistical testing is performed under a null hypothesis that the first data set and the second data set are drawn from a same continuous distribution, wherein performing the statistical testing further comprises determining a degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature; and

determine, based on the degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature, whether the plurality of test features is validated.

10 . The system of claim 9 , wherein the system is further configured to:

apply the machine learning model to the plurality of test features when the plurality of test features is validated; and

avoid applying the machine learning model to the plurality of test features when the plurality of test features is not validated.

11 . The system of claim 9 , wherein the system is further configured to:

determine a distance between a first distribution function of the first data set and a second distribution function of the second data set.

12 . The system of claim 11 , wherein the plurality of test features is determined as validated when the distance between the first distribution function of the first data set and the second distribution function of the second data set is below a threshold.

13 . The system of claim 11 , wherein the distance is determined using a Kolmogorov-Smirnov test.

14 . The system of claim 9 , wherein the system is further configured to:

fit a vector extracted from the first data set into a density estimator;

fit a vector extracted from the second data set into the density estimator; and

draw a sample from the density estimator, wherein the statistical testing is performed on the sample.

15 . The system of claim 14 , wherein the density estimator is a Gaussian density estimator.