Skip to main content

Laplace corrected modified naïve bayes model

Project description

# ModifiedNB Model

Scikit-learn based implementation of the popular cheminformatics Laplace corrected Naïve Bayes algorithm as described in:

Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases
Nidhi,†, Meir Glick,‡, John W. Davies,‡ and, and Jeremy L. Jenkins*,‡
Journal of Chemical Information and Modeling 2006 46 (3), 1124-1133
DOI: 10.1021/ci060003g

## Installation

pip install ModifiedNB

## Usage

Works exactly like any other scikit-learn model.

```python
import numpy as np
X = np.random.randint(5, size=(6, 100))
y = np.array([1, 2, 3, 4, 5, 6])

from ModifiedNB import ModifiedNB
clf = ModifiedNB()
clf.fit(X, y)
print(clf.predict(X[2:3]))
```


Project details


Release history Release notifications | RSS feed

This version

0.2

Download files

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

Source Distribution

ModifiedNB-0.2.tar.gz (2.4 kB view details)

Uploaded Source

Built Distribution

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

ModifiedNB-0.2-py3-none-any.whl (2.9 kB view details)

Uploaded Python 3

File details

Details for the file ModifiedNB-0.2.tar.gz.

File metadata

  • Download URL: ModifiedNB-0.2.tar.gz
  • Upload date:
  • Size: 2.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.21.0 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.7

File hashes

Hashes for ModifiedNB-0.2.tar.gz
Algorithm Hash digest
SHA256 818ccc96a439c7f1f15724412b4e5ea5efa0a8b0d91c3e33643831a60276b8bf
MD5 a0cc1efa38a121892c55bb672a2c6bd3
BLAKE2b-256 1b581c446d6f39b285ff51a7bf5cf0177255db4c7902534c1f4e459578d5e3ad

See more details on using hashes here.

File details

Details for the file ModifiedNB-0.2-py3-none-any.whl.

File metadata

  • Download URL: ModifiedNB-0.2-py3-none-any.whl
  • Upload date:
  • Size: 2.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.21.0 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.7

File hashes

Hashes for ModifiedNB-0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 9cdf01aa5022a6b214c44e7c52f72f91e8a27cd59d0cf200db31170d9826f757
MD5 6cb8cd4df514a94ce244dce8f807c136
BLAKE2b-256 8e4f33a3d4aba3e91c4f0d6bf4c1dffa81a59ff0ea266068613b9e49057e6af6

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