class sklearn.linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic') [source] Linear regression with combined L1 and L2 priors as regularizer. Minimizes the objective function: 1 / (2 * n_samples) * ||y - Xw||^2_2 + alpha * l1_ratio * ||w||_1 + 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2 If you are interested in controlling the L1 an

class sklearn.linear_model.BayesianRidge(n_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False, fit_intercept=True, normalize=False, copy_X=True, verbose=False) [source] Bayesian ridge regression Fit a Bayesian ridge model and optimize the regularization parameters lambda (precision of the weights) and alpha (precision of the noise). Read more in the User Guide. Parameters: n_iter : int, optional Maximum number of iterations. Default is 3

class sklearn.linear_model.ARDRegression(n_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False, threshold_lambda=10000.0, fit_intercept=True, normalize=False, copy_X=True, verbose=False) [source] Bayesian ARD regression. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights

Plot the confidence ellipsoids of each class and decision boundary print(__doc__) from scipy import linalg import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis colormap cmap = colors.LinearSegmentedColormap( 'red_blue_classes', {'red': [(0, 1, 1), (1, 0.7, 0.7)], 'green': [(0, 0.7, 0.7),

This example uses the only the first feature of the diabetes dataset, in order to illustrate a two-dimensional plot of this regression technique. The straight line can be seen in the plot, showing how linear regression attempts to draw a straight line that will best minimize the residual sum of squares between the observed responses in the dataset, and the responses predicted by the linear approximation. The coefficients, the residual sum of squares and the variance score are also calculated.

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create data points by point and click and visualize the decision region induced by different kernels and parameter settings. To create positive examples click the left mouse button; to create negative examples click the right button. If all examples are from the same class, it uses a one-class SVM. from __future__ import division, print_function print(__doc__) # Author: Peter Prettenhoer <peter.prettenho

The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a MSE criterion), yielding the Ledoit-Wolf covariance estimate. Chen et al. proposed an improvement of the Ledoit-Wolf shrinkage parameter, the OAS coefficient, whose convergence is significantly better under the assumption that the data are Gaussian. This example, inspired from Chen?s publication [1]

Warning DEPRECATED class sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001) [source] Alias for sklearn.discriminant_analysis.LinearDiscriminantAnalysis. Deprecated since version 0.17: This class will be removed in 0.19. Use sklearn.discriminant_analysis.LinearDiscriminantAnalysis instead. Methods decision_function(X) Predict confidence scores for samples. fit(X, y[, store_covariance, tol]) Fit LinearDiscriminantAnalysis mo

Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes dataset. Each color represents a different feature of the coefficient vector, and this is displayed as a function of the regularization parameter. Out: Computing regularization path using the LARS ... . print(__doc__) # Author: Fabian Pedregosa <fabian.pedregosa@inria.fr> # Alexandre Gramfort <alexandre.gramfort@inria.fr> # License: BSD 3 clause import numpy as np impor

We show that linear_model.Lasso provides the same results for dense and sparse data and that in the case of sparse data the speed is improved. print(__doc__) from time import time from scipy import sparse from scipy import linalg from sklearn.datasets.samples_generator import make_regression from sklearn.linear_model import Lasso The two Lasso implementations on Dense data print("--- Dense matrices") X, y = make_regression(n_samples=200, n_features=5000, random_state=0) X_sp = sparse.coo_m