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DeepChecks is a Python package that provides a wide variety of built-in checks to test for issues with model performance, data distribution, data integrity, and more.

In this tutorial, we will learn about DeepChecks and use it to validate the dataset and test the trained machine learning model to generate a comprehensive report. We will also learn to test models on specific tests instead of generating full reports.

Machine learning testing is essential for ensuring the reliability, fairness, and security of AI models. It helps verify model performance, detect biases, enhance security against adversarial attacks especially in Large Language Models (LLMs), ensure regulatory compliance, and enable continuous improvement. Tools like Deepchecks provide a comprehensive testing solution that addresses all aspects of AI and ML validation from research to production, making them invaluable for developing robust, trustworthy AI systems.

In this getting started guide, we will load the dataset and perform a data integrity test. This critical step ensures that our dataset is reliable and accurate, paving the way for successful model training.

It will take a few second to generate the report.

The data integrity report contains test results on:

Lets train our model and then run a model evaluation suite to learn more about model performance.

The model evaluation report contains the test results on:

There are other tests available in the suite that didn't run due to the ensemble type of model. If you ran a simple model like logistic regression, you might have gotten a full report.

If you don't want to run the entire suite of model evaluation tests, you can also test your model on a single check.

For example, you can check label drift by providing the training and testing dataset.

As a result, you will get a distribution plot and drift score.

You can even extract the value and methodology of the drift score.

The next step in your learning journey is to automate the machine learning testing process and track performance. You can do that with GitHub Actions by following the Deepchecks In CI/CD guide.

In this beginner-friendly, we have learned to generate data validation and machine learning evaluation reports using DeepChecks. If you are having trouble running the code, I suggest you have a look at the Machine Learning Testing With DeepChecks Kaggle Notebook and run it yourself.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

Read more here:
Beginners Guide to Machine Learning Testing With DeepChecks - KDnuggets