Skip to main content

Predictive imputation of missing values with sklearn interface. This is a simple implementation of the idea presented in the MissForest R package.

Project description

Predictive Imputer

https://img.shields.io/pypi/v/predictive_imputer.svg https://img.shields.io/travis/log0ymxm/predictive_imputer.svg Documentation Status Updates

Predictive imputation of missing values with sklearn interface. This is a simple implementation of the idea presented in the MissForest R package.

Features

  • Basic imputation using RandomForestRegressor

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.2.0 (2017-04-01)

  • Add new models that can be used for imputation: KNN and PCA (@founderfan)
  • Add early stopping (@founderfan)

0.1.0 (2016-11-28)

  • First release on PyPI.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for predictive_imputer, version 0.2.0
Filename, size File type Python version Upload date Hashes
Filename, size predictive_imputer-0.2.0-py2.py3-none-any.whl (5.0 kB) File type Wheel Python version 3.5 Upload date Hashes View
Filename, size predictive_imputer-0.2.0.tar.gz (12.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page