scikit-learn · jjerphan · Jan 13, 2022 · Jan 2, 2022 · Jan 2, 2022 · Jan 3, 2022
diff --git a/doc/glossary.rst b/doc/glossary.rst
@@ -1604,6 +1604,7 @@ functions or non-estimator constructors.
             number of different distinct random seeds. Popular integer
             random seeds are 0 and `42
             <https://en.wikipedia.org/wiki/Answer_to_the_Ultimate_Question_of_Life%2C_the_Universe%2C_and_Everything>`_.
+            Integer values must be in the range `[0, 2**32 - 1]`.
 
         A :class:`numpy.random.RandomState` instance
             Use the provided random state, only affecting other users

diff --git a/sklearn/linear_model/_stochastic_gradient.py b/sklearn/linear_model/_stochastic_gradient.py
@@ -955,11 +955,13 @@ class SGDClassifier(BaseSGDClassifier):
         value, the stronger the regularization.
         Also used to compute the learning rate when set to `learning_rate` is
         set to 'optimal'.
+        Values must be in the range `[0.0, inf)`.
 
     l1_ratio : float, default=0.15
         The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
         l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
         Only used if `penalty` is 'elasticnet'.
+        Values must be in the range `[0.0, 1.0]`.
 
     fit_intercept : bool, default=True
         Whether the intercept should be estimated or not. If False, the
@@ -969,6 +971,7 @@ class SGDClassifier(BaseSGDClassifier):
         The maximum number of passes over the training data (aka epochs).
         It only impacts the behavior in the ``fit`` method, and not the
         :meth:`partial_fit` method.
+        Values must be in the range `[1, inf)`.
 
         .. versionadded:: 0.19
 
@@ -978,6 +981,7 @@ class SGDClassifier(BaseSGDClassifier):
         epochs.
         Convergence is checked against the training loss or the
         validation loss depending on the `early_stopping` parameter.
+        Values must be in the range `[0.0, inf)`.
 
         .. versionadded:: 0.19
 
@@ -986,6 +990,7 @@ class SGDClassifier(BaseSGDClassifier):
 
     verbose : int, default=0
         The verbosity level.
+        Values must be in the range `[0, inf)`.
 
     epsilon : float, default=0.1
         Epsilon in the epsilon-insensitive loss functions; only if `loss` is
@@ -994,6 +999,7 @@ class SGDClassifier(BaseSGDClassifier):
         important to get the prediction exactly right.
         For epsilon-insensitive, any differences between the current prediction
         and the correct label are ignored if they are less than this threshold.
+        Values must be in the range `[0.0, inf)`.
 
     n_jobs : int, default=None
         The number of CPUs to use to do the OVA (One Versus All, for
@@ -1006,18 +1012,19 @@ class SGDClassifier(BaseSGDClassifier):
         Used for shuffling the data, when ``shuffle`` is set to ``True``.
         Pass an int for reproducible output across multiple function calls.
         See :term:`Glossary <random_state>`.
+        Integer values must be in the range `[0, 2**32 - 1]`.
 
     learning_rate : str, default='optimal'
         The learning rate schedule:
 
         - 'constant': `eta = eta0`
         - 'optimal': `eta = 1.0 / (alpha * (t + t0))`
-          where t0 is chosen by a heuristic proposed by Leon Bottou.
+          where `t0` is chosen by a heuristic proposed by Leon Bottou.
         - 'invscaling': `eta = eta0 / pow(t, power_t)`
-        - 'adaptive': eta = eta0, as long as the training keeps decreasing.
+        - 'adaptive': `eta = eta0`, as long as the training keeps decreasing.
           Each time n_iter_no_change consecutive epochs fail to decrease the
           training loss by tol or fail to increase validation score by tol if
-          early_stopping is True, the current learning rate is divided by 5.
+          `early_stopping` is `True`, the current learning rate is divided by 5.
 
             .. versionadded:: 0.20
                 Added 'adaptive' option
@@ -1026,13 +1033,15 @@ class SGDClassifier(BaseSGDClassifier):
         The initial learning rate for the 'constant', 'invscaling' or
         'adaptive' schedules. The default value is 0.0 as eta0 is not used by
         the default schedule 'optimal'.
+        Values must be in the range `(0.0, inf)`.
 
     power_t : float, default=0.5
         The exponent for inverse scaling learning rate [default 0.5].
+        Values must be in the range `(-inf, inf)`.
 
     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
+        score is not improving. If set to `True`, it will automatically set aside
         a stratified fraction of training data as validation and terminate
         training when validation score returned by the `score` method is not
         improving by at least tol for n_iter_no_change consecutive epochs.
@@ -1044,6 +1053,7 @@ class SGDClassifier(BaseSGDClassifier):
         The proportion of training data to set aside as validation set for
         early stopping. Must be between 0 and 1.
         Only used if `early_stopping` is True.
+        Values must be in the range `(0.0, 1.0)`.
 
         .. versionadded:: 0.20
             Added 'validation_fraction' option
@@ -1053,6 +1063,7 @@ class SGDClassifier(BaseSGDClassifier):
         fitting.
         Convergence is checked against the training loss or the
         validation loss depending on the `early_stopping` parameter.
+        Integer values must be in the range `[1, max_iter)`.
 
         .. versionadded:: 0.20
             Added 'n_iter_no_change' option
@@ -1081,11 +1092,12 @@ class SGDClassifier(BaseSGDClassifier):
         existing counter.
 
     average : bool or int, default=False
-        When set to True, computes the averaged SGD weights across all
+        When set to `True`, computes the averaged SGD weights across all
         updates and stores the result in the ``coef_`` attribute. If set to
         an int greater than 1, averaging will begin once the total number of
         samples seen reaches `average`. So ``average=10`` will begin
         averaging after seeing 10 samples.
+        Integer values must be in the range `[1, n_samples]`.
 
     Attributes
     ----------