DOC better doc for SGDClassifier.partial_fit
@@ -665,7 +665,12 @@ class BaseSGDClassifier(BaseSGD, LinearClassifierMixin, metaclass=ABCMeta):
self.intercept_ = self.standard_intercept_
def partial_fit(self, X, y, classes=None, sample_weight=None):
- """Fit linear model with Stochastic Gradient Descent.
+ """Perform one epoch of stochastic gradient descent on given samples.
+
+ Internally, this method uses ``max_iter = 1``. Therefore, it is not
+ guaranteed that a minimum of the cost function is reached after calling
+ it once. Matters such as objective convergence and early stopping
+ should be handled by the user.
Parameters
----------
@@ -1158,7 +1163,12 @@ class BaseSGDRegressor(BaseSGD, RegressorMixin):
return self
def partial_fit(self, X, y, sample_weight=None):
- """Fit linear model with Stochastic Gradient Descent.
+ """Perform one epoch of stochastic gradient descent on given samples.
+
+ Internally, this method uses ``max_iter = 1``. Therefore, it is not
+ guaranteed that a minimum of the cost function is reached after calling
+ it once. Matters such as objective convergence and early stopping
+ should be handled by the user.
Parameters
----------
