This is documentation for an old release of Scikit-learn (version 1.4). Try the latest stable release (version 1.6) or development (unstable) versions.

Generalized Linear Models

Examples concerning the sklearn.linear_model module.

Comparing Linear Bayesian Regressors

Comparing Linear Bayesian Regressors

Comparing various online solvers

Comparing various online solvers

Curve Fitting with Bayesian Ridge Regression

Curve Fitting with Bayesian Ridge Regression

Early stopping of Stochastic Gradient Descent

Early stopping of Stochastic Gradient Descent

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

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

HuberRegressor vs Ridge on dataset with strong outliers

HuberRegressor vs Ridge on dataset with strong outliers

Joint feature selection with multi-task Lasso

Joint feature selection with multi-task Lasso

L1 Penalty and Sparsity in Logistic Regression

L1 Penalty and Sparsity in Logistic Regression

L1-based models for Sparse Signals

L1-based models for Sparse Signals

Lasso and Elastic Net

Lasso and Elastic Net

Lasso model selection via information criteria

Lasso model selection via information criteria

Lasso model selection: AIC-BIC / cross-validation

Lasso model selection: AIC-BIC / cross-validation

Lasso on dense and sparse data

Lasso on dense and sparse data

Lasso path using LARS

Lasso path using LARS

Linear Regression Example

Linear Regression Example

Logistic Regression 3-class Classifier

Logistic Regression 3-class Classifier

Logistic function

Logistic function

MNIST classification using multinomial logistic + L1

MNIST classification using multinomial logistic + L1

Multiclass sparse logistic regression on 20newgroups

Multiclass sparse logistic regression on 20newgroups

Non-negative least squares

Non-negative least squares

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

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

Ordinary Least Squares and Ridge Regression Variance

Ordinary Least Squares and Ridge Regression Variance

Orthogonal Matching Pursuit

Orthogonal Matching Pursuit

Plot Ridge coefficients as a function of the regularization

Plot Ridge coefficients as a function of the regularization

Plot multi-class SGD on the iris dataset

Plot multi-class SGD on the iris dataset

Plot multinomial and One-vs-Rest Logistic Regression

Plot multinomial and One-vs-Rest Logistic Regression

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Polynomial and Spline interpolation

Polynomial and Spline interpolation

Quantile regression

Quantile regression

Regularization path of L1- Logistic Regression

Regularization path of L1- Logistic Regression

Ridge coefficients as a function of the L2 Regularization

Ridge coefficients as a function of the L2 Regularization

Robust linear estimator fitting

Robust linear estimator fitting

Robust linear model estimation using RANSAC

Robust linear model estimation using RANSAC

SGD: Maximum margin separating hyperplane

SGD: Maximum margin separating hyperplane

SGD: Penalties

SGD: Penalties

SGD: Weighted samples

SGD: Weighted samples

SGD: convex loss functions

SGD: convex loss functions

Sparsity Example: Fitting only features 1 and 2

Sparsity Example: Fitting only features 1 and 2

Theil-Sen Regression

Theil-Sen Regression

Tweedie regression on insurance claims

Tweedie regression on insurance claims