class sklearn.svm.LinearSVC(penalty=’l2’, loss=’squared_hinge’, dual=True, tol=0.0001, C=1.0, multi_class=’ovr’, fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)
[source]
Linear Support Vector Classification.
Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.
This class supports both dense and sparse input and the multiclass support is handled according to a onevstherest scheme.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
SVC
sklearn.multiclass.OneVsRestClassifier
wrapper. Finally SVC can fit dense data without memory copy if the input is Ccontiguous. Sparse data will still incur memory copy though.sklearn.linear_model.SGDClassifier
The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon to have slightly different results for the same input data. If that happens, try with a smaller tol
parameter.
The underlying implementation, liblinear, uses a sparse internal representation for the data that will incur a memory copy.
Predict output may not match that of standalone liblinear in certain cases. See differences from liblinear in the narrative documentation.
LIBLINEAR: A Library for Large Linear Classification
>>> from sklearn.svm import LinearSVC >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_features=4, random_state=0) >>> clf = LinearSVC(random_state=0, tol=1e5) >>> clf.fit(X, y) LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=0, tol=1e05, verbose=0) >>> print(clf.coef_) [[0.085... 0.394... 0.498... 0.375...]] >>> print(clf.intercept_) [0.284...] >>> print(clf.predict([[0, 0, 0, 0]])) [1]
decision_function (X)  Predict confidence scores for samples. 
densify ()  Convert coefficient matrix to dense array format. 
fit (X, y[, sample_weight])  Fit the model according to the given training data. 
get_params ([deep])  Get parameters for this estimator. 
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 (**params)  Set the parameters of this estimator. 
sparsify ()  Convert coefficient matrix to sparse format. 
__init__(penalty=’l2’, loss=’squared_hinge’, dual=True, tol=0.0001, C=1.0, multi_class=’ovr’, fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)
[source]
decision_function(X)
[source]
Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters: 


Returns: 

densify()
[source]
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, sample_weight=None)
[source]
Fit the model according to the given training data.
Parameters: 


Returns: 

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


Returns: 

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


Returns: 

score(X, y, sample_weight=None)
[source]
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: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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.svm.LinearSVC
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.svm.LinearSVC.html