linlearn
: linear methods in Python
linlearn
stands for linear learning. It is a scikit-learn
compatible python
package for linear learning
with Python. It provides :
Several estimators, including empirical risk minimization (which is the
standard approach), median-of-means, trimmed means among others for robust regression and classification under the presence of outliers or heavy tails in your data.
Several loss functions easily accessible from a single class (
BinaryClassifier
for binary classification andRegressor
for regression)Several penalization functions, including standard L1, ridge and elastic-net, but also total-variation, slope, weighted L1, among many others
All algorithms can use early stopping strategies during training
Supports dense and sparse data formats, and includes fast solvers for large sparse datasets (using state-of-the-art stochastic optimization algorithms)
It is accelerated thanks to numba, leading to a very concise, small, but fast library
Installation
The easiest way to install linlearn is using pip
pip install linlearn
But you can also use the latest development from github directly with
pip install git+https://github.com/linlearn/linlearn.git
References
Usage
linlearn
follows the scikit-learn API: you call fit instead of use predict_proba
or predict
whenever you need predictions.
from linlearn import BinaryClassifier
clf = BinaryClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict_proba(X_test)[:, 1]