Skip to main content

Package to easily detect or remove potential outliers

Project description

Outliers

Package for easy to use outlier detection and removal using robust random cuts forest. RRCF implementation is based on https://github.com/kLabUM/rrcf.

Project details


Download files

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

Source Distribution

ioutliers-0.1.2.tar.gz (2.4 kB view details)

Uploaded Source

File details

Details for the file ioutliers-0.1.2.tar.gz.

File metadata

  • Download URL: ioutliers-0.1.2.tar.gz
  • Upload date:
  • Size: 2.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.18.4 setuptools/51.3.1 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.7.7

File hashes

Hashes for ioutliers-0.1.2.tar.gz
Algorithm Hash digest
SHA256 5463e3ac9b24ab9dfa3f1cde6e83571750e6eeb228ac085925fc7e1675a25921
MD5 326e3461c6d8be3cdc49e5b3a87abb97
BLAKE2b-256 2033cf0569d232c441a1e675d9c70ab6e3fccd3de904e4ecace0f90f00650229

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page