glmnet-classifier 0.1.44

Contact

Rolf Carlson hrolfrc@gmail.com

Install

Use pip to install glmnet-classifier.

pip install glmnet-classifier

Introduction

The glmnet-classifier project provides GlmnetClassifier for the classification and prediction for two classes, the binomial case. GlmnetClassifier is based on glmnet. A fortran compiler is required.

GlmnetClassifier is designed for use with scikit-learn pipelines and composite estimators.

from glmnet_classifier import GlmnetClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

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)

cls = GlmnetClassifier().fit(X_train, y_train)

cls.score(X_test, y_test)

1.0

Authors

The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon. The Python package, glmnet_py, is maintained by B. J. Balakumar.

The glmnet-classifier package was written by Rolf Carlson, as an adaptation of glmnet_py.

References

References Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2008). Regularization Paths for Generalized Linear Models via Coordinate Descent Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010.

Noah Simon, Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2011). Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent Journal of Statistical Software, Vol. 39(5) 1-13.

Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, Ryan J. Tibshirani. (2010). Strong Rules for Discarding Predictors in Lasso-type Problems Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(2), 245-266.

Noah Simon, Jerome Friedman and Trevor Hastie (2013). A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression