sklearn.multiclass.OutputCodeClassifier  
class sklearn.multiclass.OutputCodeClassifier(estimator, *, code_size=1.5, random_state=None, n_jobs=None) [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.  Parameters 
 
estimatorestimator object 

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

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_stateint, RandomState instance, default=None 

The generator used to initialize the codebook. Pass an int for reproducible output across multiple function calls. See Glossary.  
n_jobsint, default=None 

The number of jobs to use for the computation: the multiclass problems are computed in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.    Attributes 
 
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.     References  
1  
“Solving multiclass learning problems via error-correcting output codes”, Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995.  
2  
“The error coding method and PICTs”, James G., Hastie T., Journal of Computational and Graphical statistics 7, 1998.  
3  
“The Elements of Statistical Learning”, Hastie T., Tibshirani R., Friedman J., page 606 (second-edition) 2008.   Examples >>> from sklearn.multiclass import OutputCodeClassifier
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=100, n_features=4,
...                            n_informative=2, n_redundant=0,
...                            random_state=0, shuffle=False)
>>> clf = OutputCodeClassifier(
...     estimator=RandomForestClassifier(random_state=0),
...     random_state=0).fit(X, y)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
 Methods  
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]) Return the mean accuracy on the given test data and labels.  
set_params(**params) Set the parameters of this estimator.    
fit(X, y) [source]
 
Fit underlying estimators.  Parameters 
 
X(sparse) array-like of shape (n_samples, n_features) 

Data.  
ynumpy array of shape [n_samples] 

Multi-class targets.    Returns 
 self
   
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

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

Parameter names mapped to their values.     
  
predict(X) [source]
 
Predict multi-class targets using underlying estimators.  Parameters 
 
X(sparse) array-like of shape (n_samples, n_features) 

Data.    Returns 
 
ynumpy array of shape [n_samples] 

Predicted multi-class targets.     
  
score(X, y, sample_weight=None) [source]
 
Return 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.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Test samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True labels for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

Mean accuracy of self.predict(X) wrt. y.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.