Generalized Linear Models

Examples concerning the sklearn.linear_model module.

Comparing various online solvers

Comparing various online solvers

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 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

Аппроксимация кривой с помощью байесовской ридж регрессии

Аппроксимация кривой с помощью байесовской ридж регрессии

Построение коэффициентов Риджа как функцию регуляризации

Построение коэффициентов Риджа как функцию регуляризации

Сравнение линейных байесовских регрессоров

Сравнение линейных байесовских регрессоров