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sklearn.svm.LinearSVC — scikit-learn 0.24.2 documentation

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 one-vs-the-rest scheme.

Read more in the User Guide.

Parameters
penalty

{‘l1’, ‘l2’}, default=’l2’

Specifies the norm used in the penalization. The ‘l2’
penalty is the standard used in SVC. The ‘l1’ leads to coef_
vectors that are sparse.

loss

{‘hinge’, ‘squared_hinge’}, default=’squared_hinge’

Specifies the loss function. ‘hinge’ is the standard SVM loss
(used e.g. by the SVC class) while ‘squared_hinge’ is the
square of the hinge loss. The combination of penalty='l1'
and loss='hinge' is not supported.

dual

bool, default=True

Select the algorithm to either solve the dual or primal
optimization problem. Prefer dual=False when n_samples > n_features.

tol

float, default=1e-4

Tolerance for stopping criteria.

C

float, default=1.0

Regularization parameter. The strength of the regularization is
inversely proportional to C. Must be strictly positive.

multi_class

{‘ovr’, ‘crammer_singer’}, default=’ovr’

Determines the multi-class strategy if y contains more than
two classes.
"ovr" trains n_classes one-vs-rest classifiers, while
"crammer_singer" optimizes a joint objective over all classes.
While crammer_singer is interesting from a theoretical perspective
as it is consistent, it is seldom used in practice as it rarely leads
to better accuracy and is more expensive to compute.
If "crammer_singer" is chosen, the options loss, penalty and dual
will be ignored.

fit_intercept

bool, default=True

Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be already centered).

intercept_scaling

float, default=1

When self.fit_intercept is True, instance vector x becomes
[x, self.intercept_scaling],
i.e. a “synthetic” feature with constant value equals to
intercept_scaling is appended to the instance vector.
The intercept becomes intercept_scaling * synthetic feature weight
Note! the synthetic feature weight is subject to l1/l2 regularization
as all other features.
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) intercept_scaling has to be increased.

class_weight

dict or ‘balanced’, default=None

Set the parameter C of class i to class_weight[i]*C for
SVC. 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)).

verbose

int, default=0

Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in liblinear that, if enabled, may not work
properly in a multithreaded context.

random_state

int, RandomState instance or None, default=None

Controls the pseudo random number generation for shuffling the data for
the dual coordinate descent (if dual=True). When dual=False the
underlying implementation of LinearSVC is not random and
random_state has no effect on the results.
Pass an int for reproducible output across multiple function calls.
See Glossary.

max_iter

int, default=1000

The maximum number of iterations to be run.

Attributes
coef_

ndarray of shape (1, n_features) if n_classes == 2 else (n_classes, n_features)

Weights assigned to the features (coefficients in the primal
problem).

coef_ is a readonly property derived from raw_coef_ that
follows the internal memory layout of liblinear.

intercept_

ndarray of shape (1,) if n_classes == 2 else (n_classes,)

Constants in decision function.

classes_

ndarray of shape (n_classes,)

The unique classes labels.

n_iter_

int

Maximum number of iterations run across all classes.

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See also

SVC

Implementation of Support Vector Machine classifier using libsvm: the kernel can be non-linear but its SMO algorithm does not scale to large number of samples as LinearSVC does. Furthermore SVC multi-class mode is implemented using one vs one scheme while LinearSVC uses one vs the rest. It is possible to implement one vs the rest with SVC by using the OneVsRestClassifier wrapper. Finally SVC can fit dense data without memory copy if the input is C-contiguous. Sparse data will still incur memory copy though.

sklearn.linear_model.SGDClassifier

SGDClassifier can optimize the same cost function as LinearSVC by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.

Notes

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.

References

LIBLINEAR: A Library for Large Linear Classification

Examples

>>>

from

sklearn.svm

import

LinearSVC

>>>

from

sklearn.pipeline

import

make_pipeline

>>>

from

sklearn.preprocessing

import

StandardScaler

>>>

from

sklearn.datasets

import

make_classification

>>>

X

,

y

=

make_classification

(

n_features

=

4

,

random_state

=

)

>>>

clf

=

make_pipeline

(

StandardScaler

(),

...

LinearSVC

(

random_state

=

,

tol

=

1e-5

))

>>>

clf

.

fit

(

X

,

y

)

Pipeline(steps=[('standardscaler', StandardScaler()),

('linearsvc', LinearSVC(random_state=0, tol=1e-05))])

>>>

print

(

clf

.

named_steps

[

'linearsvc'

]

.

coef_

)

[[0.141... 0.526... 0.679... 0.493...]]

>>>

print

(

clf

.

named_steps

[

'linearsvc'

]

.

intercept_

)

[0.1693...]

>>>

print

(

clf

.

predict

([[

,

,

,

]]))

[1]

Methods

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])

Return 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.

decision_function

(

X

)

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed
distance of that sample to the hyperplane.

Parameters
X

array-like or sparse matrix, shape (n_samples, n_features)

Samples.

Returns
array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)

Confidence scores per (sample, class) combination. In the binary
case, confidence score for self.classes_[1] where >0 means this
class would be predicted.

densify

(

)

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 no-op.

Returns
self

Fitted estimator.

fit

(

X

,

y

,

sample_weight

=

None

)

Fit the model according to the given training data.

Parameters
X

{array-like, sparse matrix} of shape (n_samples, n_features)

Training vector, where n_samples in the number of samples and
n_features is the number of features.

y

array-like of shape (n_samples,)

Target vector relative to X.

sample_weight

array-like of shape (n_samples,), default=None

Array of weights that are assigned to individual
samples. If not provided,
then each sample is given unit weight.

New in version 0.18.

Returns
self

object

An instance of the estimator.

get_params

(

deep

=

True

)

Get parameters for this estimator.

Parameters
deep

bool, default=True

If True, will return the parameters for this estimator and
contained subobjects that are estimators.

Returns
params

dict

Parameter names mapped to their values.

predict

(

X

)

Predict class labels for samples in X.

Parameters
X

array-like or sparse matrix, shape (n_samples, n_features)

Samples.

Returns
C

array, shape [n_samples]

Predicted class label per sample.

score

(

X

,

y

,

sample_weight

=

None

)

Return the mean accuracy on the given test data and labels.

In multi-label 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
X

array-like of shape (n_samples, n_features)

Test samples.

y

array-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weight

array-like of shape (n_samples,), default=None

Sample weights.

Returns
score

float

Mean accuracy of self.predict(X) wrt. y.

set_params

(

**

params

)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as Pipeline). The latter have
parameters of the form <component>__<parameter> so that it’s
possible to update each component of a nested object.

Parameters
**params

dict

Estimator parameters.

Returns
self

estimator instance

Estimator instance.

sparsify

(

)

Convert coefficient matrix to sparse format.

Converts the coef_ member to a scipy.sparse matrix, which for
L1-regularized models can be much more memory- and storage-efficient
than the usual numpy.ndarray representation.

The intercept_ member is not converted.

Returns
self

Fitted estimator.

Notes

For non-sparse 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.

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