sklearn.multioutput.MultiOutputClassifier
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class sklearn.multioutput.MultiOutputClassifier(estimator, n_jobs=None)
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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: |
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estimator : estimator object -
An estimator object implementing fit , score and predict_proba . -
n_jobs : int or None, optional (default=None) -
The number of jobs to use for the computation. It does each target variable in y in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. |
Attributes: |
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estimators_ : list of n_output estimators -
Estimators used for predictions. |
Methods
fit (X, y[, sample_weight]) | Fit the model to data. |
get_params ([deep]) | Get parameters for this estimator. |
partial_fit (X, y[, classes, sample_weight]) | Incrementally fit the model to data. |
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. |
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__init__(estimator, n_jobs=None)
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fit(X, y, sample_weight=None)
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Fit the model to data. Fit a separate model for each output variable.
Parameters: |
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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: |
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self : object |
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
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deep : boolean, optional -
If True, will return the parameters for this estimator and contained subobjects that are estimators. |
Returns: |
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params : mapping of string to any -
Parameter names mapped to their values. |
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partial_fit(X, y, classes=None, sample_weight=None)
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Incrementally fit the model to data. Fit a separate model for each output variable.
Parameters: |
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X : (sparse) array-like, shape (n_samples, n_features) -
Data. -
y : (sparse) array-like, shape (n_samples, n_outputs) -
Multi-output targets. -
classes : list of numpy arrays, shape (n_outputs) -
Each array is unique classes for one output in str/int Can be obtained by via [np.unique(y[:, i]) for i in range(y.shape[1])] , where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes . -
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: |
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self : object |
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predict(X)
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- Predict multi-output variable using a model
- trained for each target variable.
Parameters: |
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X : (sparse) array-like, shape (n_samples, n_features) -
Data. |
Returns: |
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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)
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Probability estimates. Returns prediction probabilities for each class of each output.
Parameters: |
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X : array-like, shape (n_samples, n_features) -
Data |
Returns: |
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p : array of shape = [n_samples, n_classes], or a list of n_outputs such arrays if n_outputs > 1. -
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_ . |
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score(X, y)
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“Returns the mean accuracy on the given test data and labels.
Parameters: |
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X : array-like, shape [n_samples, n_features] -
Test samples -
y : array-like, shape [n_samples, n_outputs] -
True values for X |
Returns: |
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scores : float -
accuracy_score of self.predict(X) versus y |
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set_params(**params)
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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.