In this plot you can see the training scores and validation scores of an SVM for different values of the kernel parameter gamma. For very low values of gamma, you

sklearn.metrics.auc(x, y, reorder=False)

sklearn.datasets.load_svmlight_file(f, n_features=None, dtype=, multilabel=False, zero_based='auto', query_id=False)

The

sklearn.datasets.dump_svmlight_file(X, y, f, zero_based=True, comment=None, query_id=None, multilabel=False)

class sklearn.semi_supervised.LabelPropagation(kernel='rbf', gamma=20, n_neighbors=7, alpha=1, max_iter=30, tol=0.001, n_jobs=1)

class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, precompute='auto', max_iter=1000

class sklearn.base.TransformerMixin

An example comparing the effect of reconstructing noisy fragments of a raccoon face image using firstly online

class sklearn.decomposition.SparsePCA(n_components=None, alpha=1, ridge_alpha=0.01, max_iter=1000, tol=1e-08, method='lars', n_jobs=1