# Generalized Linear Models¶

Examples concerning the `sklearn.linear_model`

module.

Comparing Linear Bayesian Regressors

Comparing various online solvers

Curve Fitting with Bayesian Ridge Regression

Early stopping of Stochastic Gradient Descent

Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples

HuberRegressor vs Ridge on dataset with strong outliers

Joint feature selection with multi-task Lasso

L1 Penalty and Sparsity in Logistic Regression

L1-based models for Sparse Signals

Lasso model selection via information criteria

Lasso model selection: AIC-BIC / cross-validation

Lasso on dense and sparse data

Logistic Regression 3-class Classifier

MNIST classification using multinomial logistic + L1

Multiclass sparse logistic regression on 20newgroups

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

Ordinary Least Squares and Ridge Regression Variance

Plot Ridge coefficients as a function of the L2 regularization

Plot Ridge coefficients as a function of the regularization

Plot multi-class SGD on the iris dataset

Plot multinomial and One-vs-Rest Logistic Regression

Poisson regression and non-normal loss

Polynomial and Spline interpolation

Regularization path of L1- Logistic Regression

Robust linear estimator fitting

Robust linear model estimation using RANSAC

SGD: Maximum margin separating hyperplane

Sparsity Example: Fitting only features 1 and 2

Tweedie regression on insurance claims