lightning.classification.LinearSVC

class lightning.classification.LinearSVC(C=1.0, loss='hinge', criterion='accuracy', max_iter=1000, tol=0.001, permute=True, shrinking=True, warm_start=False, random_state=None, callback=None, n_calls=100, verbose=0)[source]

Estimator for learning linear support vector machine by coordinate descent in the dual.

Parameters:

loss : str, ‘hinge’, ‘squared_hinge’

The loss function to be used.

criterion : str, ‘accuracy’, ‘auc’

Whether to optimize for classification accuracy or AUC.

C : float

Weight of the loss term.

max_iter : int

Maximum number of iterations to perform.

tol : float

Tolerance of the stopping criterion.

shrinking : bool

Whether to activate shrinking or not.

warm_start : bool

Whether to activate warm-start or not.

permute : bool

Whether to permute coordinates or not before cycling.

callback : callable

Callback function.

n_calls : int

Frequency with which callback must be called.

random_state : RandomState or int

The seed of the pseudo random number generator to use.

verbose : int

Verbosity level.

Methods

decision_function(X)
fit(X, y) Fit model according to X and y.
get_params([deep]) Get parameters for this estimator.
n_nonzero([percentage])
predict(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.
__init__(C=1.0, loss='hinge', criterion='accuracy', max_iter=1000, tol=0.001, permute=True, shrinking=True, warm_start=False, random_state=None, callback=None, n_calls=100, verbose=0)[source]
fit(X, y)[source]

Fit model according to X and y.

Parameters:

X : array-like, shape = [n_samples, n_features]

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

y : array-like, shape = [n_samples]

Target values.

Returns:

self : classifier

Returns self.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

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

Returns:

params : mapping of string to any

Parameter names mapped to their values.

score(X, y, sample_weight=None)

Returns 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, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

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 pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :