@@ -1302,7 +1302,7 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
13021302
13031303 batch_size : int, default='auto'
13041304 Size of minibatches for stochastic optimizers.
1305- If the solver is 'lbfgs', the classifier will not use minibatch.
1305+ If the solver is 'lbfgs', the regressor will not use minibatch.
13061306 When set to "auto", `batch_size=min(200, n_samples)`.
13071307
13081308 learning_rate : {'constant', 'invscaling', 'adaptive'}, default='constant'
@@ -1365,7 +1365,7 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
13651365 previous solution. See :term:`the Glossary <warm_start>`.
13661366
13671367 momentum : float, default=0.9
1368- Momentum for gradient descent update. Should be between 0 and 1. Only
1368+ Momentum for gradient descent update. Should be between 0 and 1. Only
13691369 used when solver='sgd'.
13701370
13711371 nesterovs_momentum : bool, default=True
@@ -1374,10 +1374,10 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
13741374
13751375 early_stopping : bool, default=False
13761376 Whether to use early stopping to terminate training when validation
1377- score is not improving. If set to true , it will automatically set
1378- aside 10% of training data as validation and terminate training when
1379- validation score is not improving by at least ``tol`` for
1380- ``n_iter_no_change`` consecutive epochs.
1377+ score is not improving. If set to True , it will automatically set
1378+ aside ``validation_fraction`` of training data as validation and
1379+ terminate training when validation score is not improving by at
1380+ least ``tol`` for ``n_iter_no_change`` consecutive epochs.
13811381 Only effective when solver='sgd' or 'adam'.
13821382
13831383 validation_fraction : float, default=0.1
@@ -1404,7 +1404,7 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
14041404
14051405 max_fun : int, default=15000
14061406 Only used when solver='lbfgs'. Maximum number of function calls.
1407- The solver iterates until convergence (determined by ' tol' ), number
1407+ The solver iterates until convergence (determined by `` tol`` ), number
14081408 of iterations reaches max_iter, or this number of function calls.
14091409 Note that number of function calls will be greater than or equal to
14101410 the number of iterations for the MLPRegressor.
@@ -1418,22 +1418,26 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
14181418
14191419 best_loss_ : float
14201420 The minimum loss reached by the solver throughout fitting.
1421- If `early_stopping=True`, this attribute is set ot `None`. Refer to
1421+ If `early_stopping=True`, this attribute is set to `None`. Refer to
14221422 the `best_validation_score_` fitted attribute instead.
1423+ Only accessible when solver='sgd' or 'adam'.
14231424
14241425 loss_curve_ : list of shape (`n_iter_`,)
14251426 Loss value evaluated at the end of each training step.
14261427 The ith element in the list represents the loss at the ith iteration.
1428+ Only accessible when solver='sgd' or 'adam'.
14271429
14281430 validation_scores_ : list of shape (`n_iter_`,) or None
14291431 The score at each iteration on a held-out validation set. The score
14301432 reported is the R2 score. Only available if `early_stopping=True`,
14311433 otherwise the attribute is set to `None`.
1434+ Only accessible when solver='sgd' or 'adam'.
14321435
14331436 best_validation_score_ : float or None
14341437 The best validation score (i.e. R2 score) that triggered the
14351438 early stopping. Only available if `early_stopping=True`, otherwise the
14361439 attribute is set to `None`.
1440+ Only accessible when solver='sgd' or 'adam'.
14371441
14381442 t_ : int
14391443 The number of training samples seen by the solver during fitting.
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