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]))
```
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]))
```
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