diff --git a/scikits/learn/multiclass.py b/scikits/learn/multiclass.py index 977cc9b02cc95040c5cfdc557e155f8f7a038980..8fcd038a74e8269ee649dd0066ea0364a29ec13e 100644 --- a/scikits/learn/multiclass.py +++ b/scikits/learn/multiclass.py @@ -242,7 +242,33 @@ class OneVsOneClassifier(BaseEstimator, ClassifierMixin): def fit_ecoc(estimator, X, y, code_size=1.5, random_state=None): - """Fit an error-correcting output-code strategy.""" + """ + Fit an error-correcting output-code strategy. + + Parameters + ---------- + estimator : estimator object + An estimator object implementing `fit` and one of `decision_function` + or `predict_proba`. + + code_size: float, optional + Percentage of the number of classes to be used to create the code book. + + random_state: numpy.RandomState, optional + The generator used to initialize the centers. Defaults to numpy.random. + + + Returns + -------- + estimators : list of `int(n_classes * code_size)` estimators + Estimators used for predictions. + + classes : numpy array of shape [n_classes] + Array containing labels. + + code_book_: numpy array of shape [n_classes, code_size] + Binary array containing the code of each class. + """ check_estimator(estimator) random_state = check_random_state(random_state) @@ -301,9 +327,12 @@ class OutputCodeClassifier(BaseEstimator, ClassifierMixin): one-vs-the-rest. A number greater than 1 will require more classifiers than one-vs-the-rest. + random_state: numpy.RandomState, optional + The generator used to initialize the centers. Defaults to numpy.random. + Attributes ---------- - estimators_ : list of `n_classes * (n_classes - 1) / 2` estimators + estimators_ : list of `int(n_classes * code_size)` estimators Estimators used for predictions. classes_ : numpy array of shape [n_classes]