8.24.2.6. sklearn.svm.sparse.LinearSVC¶
- class sklearn.svm.sparse.LinearSVC(penalty='l2', loss='l2', dual=True, tol=0.0001, C=1.0, multi_class=False, fit_intercept=True, intercept_scaling=1, scale_C=False)¶
Linear Support Vector Classification, Sparse Version
Similar to SVC with parameter kernel=’linear’, but uses internally liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should be faster for huge datasets.
See sklearn.svm.SVC for a complete list of parameters
Notes
For best results, this accepts a matrix in csr format (scipy.sparse.csr), but should be able to convert from any array-like object (including other sparse representations).
Methods
decision_function(X) Return the decision function of X according to the trained fit(X, y[, class_weight]) Fit the model using X, y as training data. fit_transform(X[, y]) Fit to data, then transform it predict(X) Predict target values of X according to the fitted model. score(X, y) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of the estimator. transform(X[, threshold]) - __init__(penalty='l2', loss='l2', dual=True, tol=0.0001, C=1.0, multi_class=False, fit_intercept=True, intercept_scaling=1, scale_C=False)¶
- decision_function(X)¶
Return the decision function of X according to the trained model.
Parameters : X : sparse matrix, shape = [n_samples, n_features]
Returns : T : array-like, shape = [n_samples, n_class]
Returns the decision function of the sample for each class in the model.
- fit(X, y, class_weight=None)¶
Fit the model using X, y as training data.
Parameters : X : sparse matrix, 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, shape = [n_samples]
Target vector relative to X
Returns : self : object
Returns an instance of self.
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters : X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns : X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
Notes
This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.
- predict(X)¶
Predict target values of X according to the fitted model.
Parameters : X : sparse matrix, shape = [n_samples, n_features] Returns : C : array, shape = [n_samples]
- score(X, y)¶
Returns the mean accuracy on the given test data and labels.
Parameters : X : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Labels for X.
Returns : z : float
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :