17458
10776
sklearn.linear_model._stochastic_gradient.SGDClassifier
sklearn.SGDClassifier
sklearn.linear_model._stochastic_gradient.SGDClassifier
1
openml==0.10.2,sklearn==0.22
Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
This estimator implements regularized linear models with stochastic
gradient descent (SGD) learning: the gradient of the loss is estimated
each sample at a time and the model is updated along the way with a
decreasing strength schedule (aka learning rate). SGD allows minibatch
(online/out-of-core) learning, see the partial_fit method.
For best results using the default learning rate schedule, the data should
have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays
of floating point values for the features. The model it fits can be
controlled with the loss parameter; by default, it fits a linear support
vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value b...
2019-12-13T20:14:52
English
sklearn==0.22
numpy>=1.6.1
scipy>=0.9
alpha
float
0.0001
Constant that multiplies the regularization term. Defaults to 0.0001
Also used to compute learning_rate when set to 'optimal'
average
bool or int
false
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.
class_weight
dict
null
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))``
early_stopping
bool
false
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
epsilon
float
0.1
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
For 'huber', determines the threshold at which it becomes less
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
eta0
double
0.0
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'
fit_intercept
bool
true
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True
l1_ratio
float
0.15
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1
Defaults to 0.15
learning_rate
str
"optimal"
The learning rate schedule:
'constant':
eta = eta0
'optimal': [default]
eta = 1.0 / (alpha * (t + t0))
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
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
loss
str
"hinge"
The loss function to be used. Defaults to 'hinge', which gives a
linear SVM
The possible options are 'hinge', 'log', 'modified_huber',
'squared_hinge', 'perceptron', or a regression loss: 'squared_loss',
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
The 'log' loss gives logistic regression, a probabilistic classifier
'modified_huber' is another smooth loss that brings tolerance to
outliers as well as probability estimates
'squared_hinge' is like hinge but is quadratically penalized
'perceptron' is the linear loss used by the perceptron algorithm
The other losses are designed for regression but can be useful in
classification as well; see SGDRegressor for a description
max_iter
int
1000
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
n_iter_no_change
int
5
Number of iterations with no improvement to wait before early stopping
.. versionadded:: 0.20
n_jobs
int or None
null
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 <n_jobs>`
for more details
penalty
str
"l2"
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'
power_t
double
0.5
The exponent for inverse scaling learning rate [default 0.5]
random_state
int
null
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`
shuffle
bool
true
Whether or not the training data should be shuffled after each epoch
Defaults to True
tol
float or None
0.001
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs
.. versionadded:: 0.19
validation_fraction
float
0.1
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
verbose
int
0
The verbosity level
warm_start
bool
false
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 <warm_start>`
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
If a dynamic learning rate is used, the learning rate is adapted
depending on the number of samples already seen. Calling ``fit`` resets
this counter, while ``partial_fit`` will result in increasing the
existing counter
openml-python
python
scikit-learn
sklearn
sklearn_0.22