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Generalized Linear Models

Examples concerning the sklearn.linear_model module.

Comparing Linear Bayesian Regressors

Comparing various online solvers

Curve Fitting with Bayesian Ridge Regression

Decision Boundaries of Multinomial and One-vs-Rest Logistic 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

Lasso, Lasso-LARS, and Elastic Net paths

Logistic function

MNIST classification using multinomial logistic + L1

Multiclass sparse logistic regression on 20newgroups

Non-negative least squares

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

Ordinary Least Squares Example

Ordinary Least Squares and Ridge Regression Variance

Orthogonal Matching Pursuit

Plot Ridge coefficients as a function of the regularization

Plot multi-class SGD on the iris dataset

Poisson regression and non-normal loss

Polynomial and Spline interpolation

Quantile regression

Regularization path of L1- Logistic Regression

Ridge coefficients as a function of the L2 Regularization

Robust linear estimator fitting

Robust linear model estimation using RANSAC

SGD: Maximum margin separating hyperplane

SGD: Weighted samples

SGD: convex loss functions

Theil-Sen Regression

Tweedie regression on insurance claims

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https://scikit-learn.org/1.6/auto_examples/linear_model/index.html