An example of estimating sources from noisy data.

Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset:

Transform a signal as a sparse combination of Ricker wavelets. This example visually compares different sparse coding methods using the

Reference: Dorin Comaniciu and Peter Meer, ?Mean Shift: A robust approach toward feature space analysis?. IEEE Transactions on Pattern Analysis

A comparison of different values for regularization parameter ?alpha? on synthetic datasets. The plot shows that different alphas yield different

This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification

This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spectral Biclustering algorithm. The data is

These figures aid in illustrating how a point cloud can be very flat in one direction?which is where PCA comes in to choose a direction that is not flat.

Partial dependence plots show the dependence between the target function

Plot the contours of the three penalties. All of the above are supported by sklearn.linear_model.stochastic_gradient.