This shows an example of a neighbors-based query (in particular a kernel density estimate) on geospatial data, using a Ball Tree built upon

sklearn.metrics.pairwise.euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False, X_norm_squared=None)

Estimates Lasso and Elastic-Net regression models on a manually generated sparse signal corrupted with an additive noise. Estimated coefficients are

class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, random_state=None)

sklearn.decomposition.dict_learning_online(X, n_components=2, alpha=1, n_iter=100, return_code=True, dict_init=None,

sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1)

class sklearn.covariance.EllipticEnvelope(store_precision=True, assume_centered=False, support_fraction=None, contamination=0.1

sklearn.preprocessing.robust_scale(X, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

A recursive feature elimination example with automatic tuning of the number of features selected with cross-validation.