class sklearn.linear_model.SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, epsilon=0.1, n_jobs=None, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False, average=False, n_iter=None)
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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/outofcore) 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 because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
>>> import numpy as np >>> from sklearn import linear_model >>> X = np.array([[1, 1], [2, 1], [1, 1], [2, 1]]) >>> Y = np.array([1, 1, 2, 2]) >>> clf = linear_model.SGDClassifier(max_iter=1000) >>> clf.fit(X, Y) ... SGDClassifier(alpha=0.0001, average=False, class_weight=None, early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=1000, n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5, random_state=None, shuffle=True, tol=None, validation_fraction=0.1, verbose=0, warm_start=False)
>>> print(clf.predict([[0.8, 1]])) [1]
decision_function (X)  Predict confidence scores for samples. 
densify ()  Convert coefficient matrix to dense array format. 
fit (X, y[, coef_init, intercept_init, …])  Fit linear model with Stochastic Gradient Descent. 
get_params ([deep])  Get parameters for this estimator. 
partial_fit (X, y[, classes, sample_weight])  Fit linear model with Stochastic Gradient Descent. 
predict (X)  Predict class labels for samples in X. 
score (X, y[, sample_weight])  Returns the mean accuracy on the given test data and labels. 
set_params (*args, **kwargs)  
sparsify ()  Convert coefficient matrix to sparse format. 
__init__(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, epsilon=0.1, n_jobs=None, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False, average=False, n_iter=None)
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decision_function(X)
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Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters: 


Returns: 

densify()
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Convert coefficient matrix to dense array format.
Converts the coef_
member (back) to a numpy.ndarray. This is the default format of coef_
and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a noop.
Returns: 


fit(X, y, coef_init=None, intercept_init=None, sample_weight=None)
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Fit linear model with Stochastic Gradient Descent.
Parameters: 


Returns: 

get_params(deep=True)
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Get parameters for this estimator.
Parameters: 


Returns: 

loss_function
DEPRECATED: Attribute loss_function was deprecated in version 0.19 and will be removed in 0.21. Use loss_function_
instead
partial_fit(X, y, classes=None, sample_weight=None)
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Fit linear model with Stochastic Gradient Descent.
Parameters: 


Returns: 

predict(X)
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Predict class labels for samples in X.
Parameters: 


Returns: 

predict_log_proba
Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss=”modified_huber”, probability estimates may be hard zeros and ones, so taking the logarithm is not possible.
See predict_proba
for details.
Parameters: 


Returns: 

predict_proba
Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (onevs.rest) estimates by simple normalization, as recommended by Zadrozny and Elkan.
Binary probability estimates for loss=”modified_huber” are given by (clip(decision_function(X), 1, 1) + 1) / 2. For other loss functions it is necessary to perform proper probability calibration by wrapping the classifier with sklearn.calibration.CalibratedClassifierCV
instead.
Parameters: 


Returns: 

Zadrozny and Elkan, “Transforming classifier scores into multiclass probability estimates”, SIGKDD‘02, http://www.research.ibm.com/people/z/zadrozny/kdd2002Transf.pdf
The justification for the formula in the loss=”modified_huber” case is in the appendix B in: http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
score(X, y, sample_weight=None)
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Returns the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: 


Returns: 

sparsify()
[source]
Convert coefficient matrix to sparse format.
Converts the coef_
member to a scipy.sparse matrix, which for L1regularized models can be much more memory and storageefficient than the usual numpy.ndarray representation.
The intercept_
member is not converted.
Returns: 


For nonsparse models, i.e. when there are not many zeros in coef_
, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum()
, must be more than 50% for this to provide significant benefits.
After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.
sklearn.linear_model.SGDClassifier
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html