# MultiOutputClassifier¶

class ibex.sklearn.multioutput.MultiOutputClassifier(estimator, n_jobs=1)

Bases: sklearn.multioutput.MultiOutputClassifier, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

estimator : estimator object
An estimator object implementing fit, score and 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. The number of jobs to use for the computation. It does each target variable in y in parallel.
estimators_ : list of n_output estimators
Estimators used for predictions.
fit(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the model to data.

Fit a separate model for each output variable.

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.
self : object
Returns self.
partial_fit(X, y, classes=None, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Incrementally fit the model to data.

Fit a separate model for each output variable.

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.
self : object
Returns self.
predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict multi-output variable using a model
trained for each target variable.
X : (sparse) array-like, shape (n_samples, n_features)
Data.
y : (sparse) array-like, shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
predict_proba(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Probability estimates.

Returns prediction probabilities for each class of each output.

X : array-like, shape (n_samples, n_features)
Data
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_.
score(X, y)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

“Returns the mean accuracy on the given test data and labels.

X : array-like, shape [n_samples, n_features]
Test samples
y : array-like, shape [n_samples, n_outputs]
True values for X
scores : float
accuracy_score of self.predict(X) versus y