Skip to main content

Detection of outlier with mahanalobis distance which have access of the parameters (means and precision matrice) with algo GMM or Bayesian GMM provide by sklearn

Project description

The author of this package has not provided a project description

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mdo_outlier_detector-0.2.2-py3-none-any.whl (4.1 kB view details)

Uploaded Python 3

File details

Details for the file mdo_outlier_detector-0.2.2-py3-none-any.whl.

File metadata

  • Download URL: mdo_outlier_detector-0.2.2-py3-none-any.whl
  • Upload date:
  • Size: 4.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.5.0.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.55.0 CPython/3.7.6

File hashes

Hashes for mdo_outlier_detector-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 763f154de3569e2efa6f184bc1d07d5f573701f1a2e49be24cd00b0cf5d561d6
MD5 5f76068b565e3c02eceb0574afb15495
BLAKE2b-256 f24aa9705041e9fa931c3554fac6838e410a34ccdce3588002c2765d6b2dbff2

See more details on using hashes here.

Supported by

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