Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort. This includes checks related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.
What Do You Need in Order to Start Validating?
Depending on your phase and what you wise to validate, you’ll need a subset of the following:
Raw data (before pre-processing such as OHE, string processing, etc.), with optional labels
The model’s training data with labels
Test data (which the model isn’t exposed to) with labels
A model compatible with scikit-learn API that you wish to validate (e.g. RandomForest, XGBoost)
Deepchecks validation accompanies you from the initial phase when you have only raw data, through the data splits, and to the final stage of having a trained model that you wish to evaluate. Accordingly, each phase requires different assets for the validation. See more about typical usage scenarios and the built-in suites in the docs.
Installation
Using pip
pip install deepchecks #--upgrade --userUsing conda
conda install -c deepchecks deepchecks1 maintainer
Metadata
1 maintainer
Release files for runml-checks 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| runml_checks-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Release files / runml_checks-1.0.0-py3-none-any.whl
| Download URL | runml_checks-1.0.0-py3-none-any.whl |
|---|---|
| Size | 484.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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