|
|
||
|---|---|---|
| .. | ||
| README.md | ||
Supprt Vector Machine
{:.no_toc}
* TOC {:toc}Top
Questions to David Rotermund
sklearn.svm.SVC
sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)
kernel : {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’
Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape (n_samples, n_samples).
fit
fit(X, y, sample_weight=None)
Fit the SVM model according to the given training data.
X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)
Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).
y : array-like of shape (n_samples,)
Target values (class labels in classification, real numbers in regression).
predict
predict(X)
For an one-class model, +1 or -1 is returned.
Parameters:
X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train)
Returns:
y_pred : ndarray of shape (n_samples,)
Class labels for samples in X.