Deepchecks Logo Deepchecks Suite of Checks

Welcome to Deepchecks!#

Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompanies you through various validation and testing needs such as verifying your dataโ€™s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models.

Note

In addition to perusing the documentation, please feel free to to ask questions on our Slack Community, or to post a issue or start a discussion on Github.

โฉ Getting Started#

Head over to the Getting Started section to learn how to get up and running with deepchecks in 5 minutes.

๐Ÿƒโ€โ™€๏ธ See It in Action#

For a quick start, check out the following pages in the tutorials section:

Tabular Data Quickstart#

Computer Vision Quickstart#

CV Beta Release

Note

Deepchecksโ€™ Computer Vision subpackage is in beta release. It is available for installation from PyPi, use at your own discretion. Github Issues are welcome!

๐Ÿ™‹๐Ÿผ When Should You Use Deepchecks?#

While youโ€™re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it.

When To Validate - ML Pipeline Schema

See the When Should You Use Section for an elaborate explanation of the typical scenarios.

๐Ÿ”Ž See More#

For additional usage examples and for understanding the best practices of how to use the package, stay tuned, as this package is in active development!