This example demonstrates the problems of underfitting and overfitting and how we can use linear regression with polynomial features to approximate nonlinear functions

class sklearn.kernel_approximation.Nystroem(kernel='rbf', gamma=None, coef0=1, degree=3, kernel_params=None, n_components=100

Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary print(__doc__)

This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see

The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations

This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the Baye

sklearn.metrics.pairwise.linear_kernel(X, Y=None)

This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint

class sklearn.linear_model.RidgeClassifier(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001

This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocation on a