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Merged
merged 9 commits into from
Feb 7, 2023
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DOC Update MLPRegressor docs #25556

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modified: sklearn/neural_network/_multilayer_perceptron.py
it176131 Feb 6, 2023
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modified: sklearn/neural_network/_multilayer_perceptron.py
it176131 Feb 6, 2023
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modified: sklearn/neural_network/_multilayer_perceptron.py
it176131 Feb 6, 2023
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modified: sklearn/neural_network/_multilayer_perceptron.py
it176131 Feb 6, 2023
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modified: sklearn/neural_network/_multilayer_perceptron.py
it176131 Feb 6, 2023
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modified: sklearn/neural_network/_multilayer_perceptron.py
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modified: sklearn/neural_network/_multilayer_perceptron.py
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Merge branch 'main' into mlpregressor_docs
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Merge branch 'main' into mlpregressor_docs
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20 changes: 12 additions & 8 deletions sklearn/neural_network/_multilayer_perceptron.py
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Expand Up @@ -1302,7 +1302,7 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):

batch_size : int, default='auto'
Size of minibatches for stochastic optimizers.
If the solver is 'lbfgs', the classifier will not use minibatch.
If the solver is 'lbfgs', the regressor will not use minibatch.
When set to "auto", `batch_size=min(200, n_samples)`.

learning_rate : {'constant', 'invscaling', 'adaptive'}, default='constant'
Expand Down Expand Up @@ -1365,7 +1365,7 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
previous solution. See :term:`the Glossary <warm_start>`.

momentum : float, default=0.9
Momentum for gradient descent update. Should be between 0 and 1. Only
Momentum for gradient descent update. Should be between 0 and 1. Only
used when solver='sgd'.

nesterovs_momentum : bool, default=True
Expand All @@ -1374,10 +1374,10 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):

early_stopping : bool, default=False
Whether to use early stopping to terminate training when validation
score is not improving. If set to true, it will automatically set
aside 10% of training data as validation and terminate training when
validation score is not improving by at least ``tol`` for
``n_iter_no_change`` consecutive epochs.
score is not improving. If set to True, it will automatically set
aside ``validation_fraction`` of training data as validation and
terminate training when validation score is not improving by at
least ``tol`` for ``n_iter_no_change`` consecutive epochs.
Only effective when solver='sgd' or 'adam'.

validation_fraction : float, default=0.1
Expand All @@ -1404,7 +1404,7 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):

max_fun : int, default=15000
Only used when solver='lbfgs'. Maximum number of function calls.
The solver iterates until convergence (determined by 'tol'), number
The solver iterates until convergence (determined by ``tol``), number
of iterations reaches max_iter, or this number of function calls.
Note that number of function calls will be greater than or equal to
the number of iterations for the MLPRegressor.
Expand All @@ -1418,22 +1418,26 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):

best_loss_ : float
The minimum loss reached by the solver throughout fitting.
If `early_stopping=True`, this attribute is set ot `None`. Refer to
If `early_stopping=True`, this attribute is set to `None`. Refer to
the `best_validation_score_` fitted attribute instead.
Only accessible when solver='sgd' or 'adam'.

loss_curve_ : list of shape (`n_iter_`,)
Loss value evaluated at the end of each training step.
The ith element in the list represents the loss at the ith iteration.
Only accessible when solver='sgd' or 'adam'.

validation_scores_ : list of shape (`n_iter_`,) or None
The score at each iteration on a held-out validation set. The score
reported is the R2 score. Only available if `early_stopping=True`,
otherwise the attribute is set to `None`.
Only accessible when solver='sgd' or 'adam'.

best_validation_score_ : float or None
The best validation score (i.e. R2 score) that triggered the
early stopping. Only available if `early_stopping=True`, otherwise the
attribute is set to `None`.
Only accessible when solver='sgd' or 'adam'.

t_ : int
The number of training samples seen by the solver during fitting.
Expand Down