skmultilearn.base.base module¶
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class
skmultilearn.base.base.
MLClassifierBase
[source]¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.ClassifierMixin
Base class providing API and common functions for all multi-label classifiers.
Implements base functionality for ML classifiers, especially the get/set params for scikit-learn compatibility.
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fit
(X, y)[source]¶ Abstract method to fit classifier with training data
It must return a fitted instance of
self
.Parameters: - X (numpy.ndarray or scipy.sparse) – input features, can be a dense or sparse matrix of size
(n_samples, n_features)
- y (numpy.ndaarray or scipy.sparse {0,1}) – binary indicator matrix with label assignments.
Returns: fitted instance of self
Return type: Raises: NotImplementedError
– this is just an abstract method- X (numpy.ndarray or scipy.sparse) – input features, can be a dense or sparse matrix of size
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get_params
(deep=True)[source]¶ Get parameters to sub-objects
Introspection of classifier for search models like cross-validation and grid search.
Parameters: deep (bool) – if True
all params will be introspected also and appended to the output dictionary.Returns: out – dictionary of all parameters and their values. If deep=True
the dictionary also holds the parameters of the parameters.Return type: dict
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predict
(X)[source]¶ Abstract method to predict labels
Parameters: X (numpy.ndarray or scipy.sparse.csc_matrix) – input features of shape (n_samples, n_features)
Returns: binary indicator matrix with label assignments with shape (n_samples, n_labels)
Return type: scipy.sparse of int Raises: NotImplementedError
– this is just an abstract method
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