class sklearn.manifold.SpectralEmbedding(n_components=2, affinity='nearest_neighbors', gamma=None, random_state=None, eigen_solver=None

sklearn.metrics.v_measure_score(labels_true, labels_pred)

Example of Precision-Recall metric to evaluate classifier output quality. In information retrieval, precision is a measure of result relevancy, while recall is a measure

class sklearn.linear_model.PassiveAggressiveClassifier(C=1.0, fit_intercept=True, n_iter=5, shuffle=True, verbose=0

Toy example of 1D regression using linear, polynomial and RBF kernels. print(__doc__)

The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for

sklearn.metrics.pairwise_distances(X, Y=None, metric='euclidean', n_jobs=1, **kwds)

The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers in the data set. In such a case, it would be better to

sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)

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