@@ -152,7 +152,6 @@ class OneVsRestClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
152152        or `predict_proba`. 
153153
154154    n_jobs : int, optional, default: 1 
155- 
156155        The number of jobs to use for the computation. If -1 all CPUs are used. 
157156        If 1 is given, no parallel computing code is used at all, which is 
158157        useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are 
@@ -316,6 +315,7 @@ def predict_ovo(estimators, classes, X):
316315            votes [pred  ==  0 , i ] +=  1 
317316            votes [pred  ==  1 , j ] +=  1 
318317            k  +=  1 
318+ 
319319    # find all places with maximum votes per sample 
320320    maxima  =  votes  ==  np .max (votes , axis = 1 )[:, np .newaxis ]
321321
@@ -347,7 +347,6 @@ class OneVsOneClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
347347        An estimator object implementing `fit` and `predict`. 
348348
349349    n_jobs : int, optional, default: 1 
350- 
351350        The number of jobs to use for the computation. If -1 all CPUs are used. 
352351        If 1 is given, no parallel computing code is used at all, which is 
353352        useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are 
@@ -498,7 +497,6 @@ class OutputCodeClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
498497        numpy.random. 
499498
500499    n_jobs : int, optional, default: 1 
501- 
502500        The number of jobs to use for the computation. If -1 all CPUs are used. 
503501        If 1 is given, no parallel computing code is used at all, which is 
504502        useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are 
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