sklearn.svm.libsvm.predict_proba() Predict probabilities svm_model stores all parameters needed to

class sklearn.decomposition.TruncatedSVD(n_components=2, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)

sklearn.datasets.fetch_20newsgroups(data_home=None, subset='train', categories=None, shuffle=True, random_state=42, remove=()

class sklearn.linear_model.MultiTaskLasso(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001

class sklearn.tree.ExtraTreeClassifier(criterion='gini', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1

class sklearn.model_selection.LeaveOneGroupOut

sklearn.metrics.coverage_error(y_true, y_score, sample_weight=None)

class sklearn.exceptions.FitFailedWarning

class sklearn.base.BaseEstimator

class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True