class sklearn.preprocessing.Binarizer(threshold=0.0, copy=True)

2.5.1. Principal component analysis (PCA) 2.5.1.1. Exact PCA and probabilistic interpretation PCA

sklearn.datasets.make_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, random_state=None)

This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the

sklearn.datasets.load_sample_images()

class sklearn.model_selection.ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)

sklearn.linear_model.orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=None, tol=None, norms_squared=None, copy_Gram=True, copy_Xy=True

sklearn.cluster.spectral_clustering(affinity, n_clusters=8, n_components=None, eigen_solver=None, random_state=None, n_init=10

class sklearn.linear_model.Lasso(alpha=1.0, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001

Shown in the plot is how the logistic regression would, in this synthetic dataset, classify values as either 0 or 1, i.e. class one or two, using the logistic curve.