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class sklearn.multioutput.MultiOutputClassifier(estimator, n_jobs=1)[source] -
Multi target classification
This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification
Parameters: estimator : estimator object
An estimator object implementing
fit,scoreandpredict_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. The number of jobs to use for the computation. It does each target variable in y in parallel.
Attributes: estimators_ : list of
n_outputestimatorsEstimators used for predictions.
Methods
fit(X, y[, sample_weight])Fit the model to data. get_params([deep])Get parameters for this estimator. predict(X)Predict multi-output variable using a model trained for each target variable. predict_proba(X)Probability estimates. score(X, y)?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)[source]
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fit(X, y, sample_weight=None)[source] -
Fit the model to data. Fit a separate model for each output variable.
Parameters: X : (sparse) array-like, shape (n_samples, n_features)
Data.
y : (sparse) array-like, shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel estimation.
sample_weight : array-like, shape = (n_samples) or None
Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
Returns: self : object
Returns self.
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get_params(deep=True)[source] -
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.
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predict(X)[source] -
- Predict multi-output variable using a model
- trained for each target variable.
Parameters: X : (sparse) array-like, shape (n_samples, n_features)
Data.
Returns: y : (sparse) array-like, shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
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predict_proba(X)[source] -
Probability estimates. Returns prediction probabilites for each class of each output.
Parameters: X : array-like, shape (n_samples, n_features)
Data
Returns: T : (sparse) array-like, shape = (n_samples, n_classes, n_outputs)
The class probabilities of the samples for each of the outputs
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score(X, y)[source] -
?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, n_outputs]
True values for X
Returns: scores : float
accuracy_score of self.predict(X) versus y
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set_params(**params)[source] -
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it?s possible to update each component of a nested object.Returns: self :
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multioutput.MultiOutputClassifier()
2017-01-15 04:24:27
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