One-time training with scikit-learn

Question

Say I have only 1 positive for classifier training. Is there a way to train a model with scikit-learn with only one positive? (for example, similar to SVM).

I currently have the following:

scores = [
   ('precision', precision_score),
]

for score_name, score_func in scores:
    clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=score_func)
    clf.fit(X[train], y[train])
    y_true, y_pred = y[test], clf.predict(X[test])

But I get the following error:

ValueError: The least populated class in y has only 2 members, which are too few. The minimum number of labels for any class cannot be less than k = 3.

Answer 1

Scikit-learn does not have a one-time training model.

, , GridSearchCV - , , 2 .