High performance Python GLMs with all the features!

Python implementation of elastic-net regularized generalized linear models

A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers https://arxiv.org/abs/1802.00124

L1-regularized least squares with PyTorch

This is implementation Lasso with Coordinate Descent and LARS (Least Angle Regression).

Image inpainting via dictionary learning and sparse representation.

Convex optimizers for LASSO, including subgradient, project gradient, proximal gradient, smooth method, lagrangian method and stochastic gradient descent variants.

Group elastic net implementation in PyTorch.

Lasso Quantile Regression

An implementation of LassoNet for arbitrary network architectures

Implementation of Relaxed Lasso Algorithm for Linear Regression.

Dusted Bot is an automated tool designed to interact with the Dusted app's API to play the Lasso game and claim MON tokens

glmnet for python

An unsupervised compressed-sensing technique for fundamental objects selection

Analysis of NBA player stats and salaries of the 2016-17 for the 17-18 season

STELA algorithm for sparsity regularized linear regression (LASSO)

This is the implementation of the five regression methods Least Square (LS), Regularized Least Square (RLS), LASSO, Robust Regression (RR) and Bayesian Regression (BR).

Feature selection comparison in breath cancer dataset