tedana.selection._utils.do_svm¶
-
do_svm(X_train, y_train, X_test, svmtype=0)[source]¶ Implements Support Vector Classification on provided data
Parameters: - X_train ((N1 x F) array_like) – Training vectors, where n_samples is the number of samples in the training dataset and n_features is the number of features.
- y_train ((N1,) array_like) – Target values (class labels in classification, real numbers in regression)
- X_test ((N2 x F) array_like) – Test vectors, where n_samples is the number of samples in the test dataset and n_features is the number of features.
- svmtype (
int, optional) – Desired support vector machine type. Must be in [0, 1, 2]. Default: 0
Returns: - y_pred ((N2,)
numpy.ndarray) – Predicted class labels for samples in X_test - clf ({
sklearn.svm.SVC,sklearn.svm.LinearSVC}) – Trained sklearn model instance