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What is unit testing in machine learning?
A unit test checks that a specific part of the code functions as expected. This is a well-established part of software development, so the tools and frameworks are very mature. Machine Learning has code for the pipeline, model training, model testing, validation, and serving.
How do you test for machine learning models?
How to write model tests?
- Check the general logic of the model (not possible in the case of deep neural networks so go to the next step if working with a DL model).
- Control the model performance by manual testing for a random couple of data points.
- Evaluate the accuracy of the ML model.
What is done during the testing of machine learning model?
In machine learning, model testing is referred to as the process where the performance of a fully trained model is evaluated on a testing set. There are a number of statistical metrics that can be used to assess testing results including mean squared errors and receiver operating characteristics curves.
How do you write a unit test case in python?
Unit tests are usually written as a separate code in a different file, and there could be different naming conventions that you could follow. You could either write the name of the unit test file as the name of the code/unit + test separated by an underscore or test + name of the code/unit separated by an underscore.
Are there any unit tests for machine learning?
Machine Learning has code for the pipeline, model training, model testing, validation, and serving. You could unit test the training and testing code, but I do not think it is worth the time. In my opinion, it is a bit redundant. The pipeline and serving are worth the time to add unit tests.
How to test a machine learning model without regularization?
Train your algorithm without regularization. If your model is complex enough, it will memorize the training data and your training loss will be close to 0. Test specific subcomputations of your algorithm. For example, you can test that a part of your RNN runs once per element of the input data.
How to test for continuous coverage in machine learning?
To run integration tests faster, train on a subset of the data or with a simpler model. The details depend on your model and data. To get continuous coverage, you’d adjust your faster tests so that they run with every new version of model or software. Meanwhile, your slow tests would run continuously in the background.
When to run integration tests in machine learning?
Check that components work together by writing a test that runs the entire pipeline end-to-end. Such a test is called an integration test. Besides running integration tests continuously, you should run integration tests when pushing new models and new software versions.