Generalized Linear Models#
Examples concerning the sklearn.linear_model
module.
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Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
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HuberRegressor vs Ridge on dataset with strong outliers
HuberRegressor vs Ridge on dataset with strong outliers
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MNIST classification using multinomial logistic + L1
MNIST classification using multinomial logistic + L1
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Multiclass sparse logistic regression on 20newgroups
Multiclass sparse logistic regression on 20newgroups
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One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
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Ordinary Least Squares and Ridge Regression Variance
Ordinary Least Squares and Ridge Regression Variance
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Plot Ridge coefficients as a function of the regularization
Plot Ridge coefficients as a function of the regularization
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Plot multinomial and One-vs-Rest Logistic Regression
Plot multinomial and One-vs-Rest Logistic Regression
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Ridge coefficients as a function of the L2 Regularization
Ridge coefficients as a function of the L2 Regularization