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 accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran.

Features

  • The existing suites include default conditions added for most of the checks
  • You can edit the preconfigured suites or build a suite of your own with a collection of checks and optional conditions
  • The model's training data with labels
  • Test data (which the model isn't exposed to) with labels
  • The package currently supports tabular data
  • A Suite runs a collection of Checks with optional Conditions added to them

Project Samples

Project Activity

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License

GNU Free Documentation License

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Additional Project Details

Programming Language

Python

Related Categories

Python Software Testing Tool, Python Machine Learning Software, Python CI CD

Registered

2022-08-10