Factual package intelligence from PyPI

pydrift 0.2.15 package intelligence

How do we measure the degradation of a machine learning process? Why does the performance of our predictive models decrease? Maybe it is that a data source has changed (one or more variables) or maybe what changes is the relationship of these variables with the target we want to

pip install pydrift

Current version
0.2.15
Python requirement
>=3.6.1
License
MIT
Distribution type
Pure Python wheel
Release files
2 (1 wheels, 1 source)
Download size
33.5 KiB
Release history
23 releases with files
Median release cadence
4 days

pydrift dependencies

PyPI declares 9 unique dependency rules for this release. Environment markers are shown when supplied by the project.

  1. pandas >=1.0.3
  2. scikit-learn >=0.23.1
  3. typing-extensions >=3.7.4 when python_version < "3.8"
  4. catboost >=0.23
  5. llvmlite >0.33.0
  6. numba >=0.51.2
  7. shap >=0.35.0
  8. statsmodels >=0.11.1

Compatibility and release files

pydrift publishes 1 wheel and 1 source archive for version 0.2.15. Wheel platform tags: any.

Declared Python classifiers: 3.7.

Release activity and sources

PyPI lists 23 releases with files. The first dated release is ; 7 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .