Testing of Machine learning models

  1. Dual coding: It means the same data will be applied to various models. So think of business rule being generated using decision tree and Random forest and artificial neural network. We observe that whether for all of these models in case of same input whether the output value lies almost in the same range.
    If the value does not vary much we can say that our rule generation approach is proper. For example, in the case of face recognition software, the approach or features selected to determine a face will be mostly same irrespective of the model being chosen.
  2. Metamorphic relationship: In the case of any ML model we need to find the metamorphic relation that exists. For example in the case of machine learning software that determines the chances of developing heart disease is there. Now we determine that if a person is aged more than 30 years and smokes then chances to have heart disease is increased by 5 %. Now when training data or test data is being passed; the percentage should come as 5 % for age 30, 10 % for the age of 35 and above, and 15 % for the age of 40 and above. If at any level if this fails then that is a bug.
  3. Coverage Guided Fuzzing(CGF): Coverage guided fuzzing is something which is used effectively for conventional software. In the case of conventional software, various fuzzed input is being given so that all paths of code are being traversed and hence coverage increased and so does the bug detection.
    Now suppose there is one ML model which is based on a neural network. Then the neurons can be considered similar to various paths of conventional software. The aim of fuzzing here is to activate all the neurons by various inputs and thus identify the corresponding bug.
  1. Input fuzzer.
  2. Mutator
  3. Analyzer.




Productivity Engineer at Narvar . Data Science enthusiast. Boxer by passion

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Akash Das

Akash Das

Productivity Engineer at Narvar . Data Science enthusiast. Boxer by passion

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