Fork me on GitHub


class sklearn.multiclass.OutputCodeClassifier(estimator, code_size=1.5, random_state=None, n_jobs=1)[source]

(Error-Correcting) Output-Code multiclass strategy

Output-code based strategies consist in representing each class with a binary code (an array of 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At prediction time, the classifiers are used to project new points in the class space and the class closest to the points is chosen. The main advantage of these strategies is that the number of classifiers used can be controlled by the user, either for compressing the model (0 < code_size < 1) or for making the model more robust to errors (code_size > 1). See the documentation for more details.

Read more in the User Guide.


estimator : estimator object

An estimator object implementing fit and one of decision_function or predict_proba.

code_size : float

Percentage of the number of classes to be used to create the code book. A number between 0 and 1 will require fewer classifiers than 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 codebook. Defaults to numpy.random.

n_jobs : int, optional, default: 1

The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.


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.


[R186]“Solving multiclass learning problems via error-correcting output codes”, Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995.
[R187]“The error coding method and PICTs”, James G., Hastie T., Journal of Computational and Graphical statistics 7, 1998.
[R188]“The Elements of Statistical Learning”, Hastie T., Tibshirani R., Friedman J., page 606 (second-edition) 2008.


fit(X, y) Fit underlying estimators.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict multi-class targets using underlying estimators.
score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
__init__(estimator, code_size=1.5, random_state=None, n_jobs=1)[source]
fit(X, y)[source]

Fit underlying estimators.


X : (sparse) array-like, shape = [n_samples, n_features]


y : numpy array of shape [n_samples]

Multi-class targets.


self :


Get parameters for this estimator.


deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.


params : mapping of string to any

Parameter names mapped to their values.


Predict multi-class targets using underlying estimators.


X : (sparse) array-like, shape = [n_samples, n_features]



y : numpy array of shape [n_samples]

Predicted multi-class targets.

score(X, y, sample_weight=None)[source]

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.


X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.


score : float

Mean accuracy of self.predict(X) wrt. y.


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :