class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto',
class sklearn.linear_model.TheilSenRegressor(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None,
class sklearn.kernel_ridge.KernelRidge(alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None)
This example studies the scalability profile of approximate 10-neighbors queries using the LSHForest with n_estimators=20 and
class sklearn.ensemble.ExtraTreesRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1
sklearn.metrics.pairwise.paired_euclidean_distances(X, Y)
Compares FeatureHasher and DictVectorizer by using both to vectorize text documents. The example demonstrates syntax and speed only; it doesn
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
Plot several randomly generated 2D classification datasets. This example illustrates the datasets.make_classification datasets
class sklearn.random_projection.SparseRandomProjection(n_components='auto', density='auto', eps=0.1, dense_output=False
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