DOC Update MLPRegressor docs
@@ -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'
@@ -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
@@ -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.
@@ -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.
