| C | Maximum step size (regularization). Defaults to 1.0 | default: 1.0 |
| average | When set to True, computes the averaged SGD weights 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
.. versionadded:: 0.19
parameter *average* to use weights averaging in SGD. | default: false |
| class_weight | Preset for the class_weight fit parameter
Weights associated with classes. If not given, all classes
are supposed to have weight one
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
.. versionadded:: 0.17
parameter *class_weight* to automatically weight samples | default: null |
| early_stopping | Whether to use early stopping to terminate training when validation
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 is not improving by at least tol for
n_iter_no_change consecutive epochs
.. versionadded:: 0.20 | default: false |
| fit_intercept | Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered | default: true |
| loss | The loss function to be used:
hinge: equivalent to PA-I in the reference paper
squared_hinge: equivalent to PA-II in the reference paper | default: "hinge" |
| max_iter | 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
.. versionadded:: 0.19 | default: 1000 |
| n_iter_no_change | Number of iterations with no improvement to wait before early stopping
.. versionadded:: 0.20 | default: 5 |
| n_jobs | The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary `
for more details | default: null |
| random_state | Used to shuffle the training data, when ``shuffle`` is set to
``True``. Pass an int for reproducible output across multiple
function calls
See :term:`Glossary ` | default: null |
| shuffle | Whether or not the training data should be shuffled after each epoch | default: true |
| tol | The stopping criterion. If it is not None, the iterations will stop
when (loss > previous_loss - tol)
.. versionadded:: 0.19 | default: 0.001 |
| validation_fraction | 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
.. versionadded:: 0.20 | default: 0.1 |
| verbose | The verbosity level | default: 0 |
| warm_start | When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution
See :term:`the Glossary `
Repeatedly calling fit or partial_fit when warm_start is True can
result in a different solution than when calling fit a single time
because of the way the data is shuffled | default: false |