sklearn.multiclass.OneVsOneClassifier¶
- class sklearn.multiclass.OneVsOneClassifier(estimator, n_jobs=1)¶
One-vs-one multiclass strategy
This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit n_classes * (n_classes - 1) / 2 classifiers, this method is usually slower than one-vs-the-rest, due to its O(n_classes^2) complexity. However, this method may be advantageous for algorithms such as kernel algorithms which don’t scale well with n_samples. This is because each individual learning problem only involves a small subset of the data whereas, with one-vs-the-rest, the complete dataset is used n_classes times.
Parameters: estimator : estimator object
An estimator object implementing fit and one of decision_function or predict_proba.
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.
Attributes: `estimators_` : list of n_classes * (n_classes - 1) / 2 estimators
Estimators used for predictions.
`classes_` : numpy array of shape [n_classes]
Array containing labels.
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]) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of this estimator. - __init__(estimator, n_jobs=1)¶
- fit(X, y)¶
Fit underlying estimators.
Parameters: X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
y : numpy array of shape [n_samples]
Multi-class targets.
Returns: self :
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- predict(X)¶
Predict multi-class targets using underlying estimators.
Parameters: X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Data.
Returns: y : numpy array of shape [n_samples]
Predicted multi-class targets.
- score(X, y, sample_weight=None)¶
Returns the mean accuracy on the given test data and labels.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples,)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
Mean accuracy of self.predict(X) wrt. y.
- set_params(**params)¶
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 :