8000 [MRG+1] docs(MLPClassifier): add multi-label support in fit docstring by alexandercbooth · Pull Request #7974 · scikit-learn/scikit-learn · GitHub
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[MRG+1] docs(MLPClassifier): add multi-label support in fit docstring #7974

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Dec 6, 2016
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[MRG+1] docs(MLPClassifier): add multi-label support in fit docstring #7974

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31 changes: 16 additions & 15 deletions sklearn/neural_network/multilayer_perceptron.py
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Original file line number Diff line number Diff line change
Expand Up @@ -601,15 +601,16 @@ def _update_no_improvement_count(self, early_stopping, X_val, y_val):
self.best_loss_ = self.loss_curve_[-1]

def fit(self, X, y):
"""Fit the model to data matrix X and target y.
"""Fit the model to data matrix X and target(s) y.

Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
X : array-like or sparse matrix, shape (n_samples, n_features)
The input data.

y : array-like, shape (n_samples,)
The target values.
y : array-like, shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).

Returns
-------
Expand Down Expand Up @@ -818,17 +819,17 @@ class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):

Attributes
----------
`classes_` : array or list of array of shape (n_classes,)
classes_ : array or list of array of shape (n_classes,)
Class labels for each output.

`loss_` : float
loss_ : float
The current loss computed with the loss function.

`coefs_` : list, length n_layers - 1
coefs_ : list, length n_layers - 1
The ith element in the list represents the weight matrix corresponding
to layer i.

`intercepts_` : list, length n_layers - 1
intercepts_ : list, length n_layers - 1
The ith element in the list represents the bias vector corresponding to
layer i + 1.

Expand All @@ -838,10 +839,10 @@ class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):
n_layers_ : int
Number of layers.

`n_outputs_` : int
n_outputs_ : int
Number of outputs.

`out_activation_` : string
out_activation_ : string
Name of the output activation function.

Notes
Expand Down Expand Up @@ -1163,14 +1164,14 @@ class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin):

Attributes
----------
`loss_` : float
loss_ : float
The current loss computed with the loss function.

`coefs_` : list, length n_layers - 1
coefs_ : list, length n_layers - 1
The ith element in the list represents the weight matrix corresponding
to layer i.

`intercepts_` : list, length n_layers - 1
intercepts_ : list, length n_layers - 1
The ith element in the list represents the bias vector corresponding to
layer i + 1.

Expand All @@ -1180,10 +1181,10 @@ class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin):
n_layers_ : int
Number of layers.

`n_outputs_` : int
n_outputs_ : int
Number of outputs.

`out_activation_` : string
out_activation_ : string
Name of the output activation function.

Notes
Expand Down
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