Examples concerning the sklearn.linear_model module.
sklearn.linear_model
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: Penalties
SGD: Weighted samples
SGD: convex loss functions
Theil-Sen Regression
Tweedie regression on insurance claims
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