Coarse approximation linear function with cross validation
Project description
CalfCV
A binomial classifier that implements the Coarse Approximation Linear Function (CALF).
Contact
Rolf Carlson hrolfrc@gmail.com
Install
Use pip to install calfcv.
pip install calfcv
Introduction
This is a python implementation of the Coarse Approximation Linear Function (CALF). The implementation is based on the greedy forward selection algorithm described in the paper referenced below.
Currently, CalfCV provides classification and prediction for two classes, the binomial case. Multinomial classification with more than two cases is not implemented.
The feature matrix is scaled to have zero mean and unit variance. Cross-validation is implemented to identify optimal score and coefficients. CalfCV is designed for use with scikit-learn pipelines and composite estimators.
Example
from calfcv import CalfCV
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_splitMake a classification problem
seed = 42
X, y = make_classification(
n_samples=30,
n_features=5,
n_informative=2,
n_redundant=2,
n_classes=2,
random_state=seed
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed)Train the classifier
cls = CalfCV().fit(X_train, y_train)Get the score on unseen data
cls.score(X_test, y_test)0.875
References
Jeffries, C.D., Ford, J.R., Tilson, J.L. et al. A greedy regression algorithm with coarse weights offers novel advantages. Sci Rep 12, 5440 (2022). https://doi.org/10.1038/s41598-022-09415-2
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