sklearn.pipeline.make_pipeline(*steps)

class sklearn.linear_model.MultiTaskElasticNetCV(l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True

class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0

class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False)

class sklearn.feature_extraction.text.CountVectorizer(input=u'content', encoding=u'utf-8', decode_error=u'strict',

class sklearn.covariance.ShrunkCovariance(store_precision=True, assume_centered=False, shrinkage=0.1)

class sklearn.mixture.GaussianMixture(n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1,

class sklearn.kernel_ridge.KernelRidge(alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None)

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

sklearn.cluster.k_means(X, n_clusters, init='k-means++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=0.0001