MLPClassifier threshold factor to eliminate test samples that are not in match with train data I am using MLPClassifer example from
scikit-learn

The code for training: 

from sklearn.neural_network import MLPClassifier
X = [[0., 0.], [1., 1.]]
y = [0, 1]
clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
                    hidden_layer_sizes=(5, 2), random_state=1)

clf.fit(X, y)                         


At the predict step, we use test data [2., 2.], [-1., -2.] in
clf.predict([[2., 2.], [-1., -2.]]). The output of this function is
array([1, 0])

As we observe, the test data [2.,2.] is not in the train dataset we passed. Still, we got the closest match as label 1. 

What i am trying to find is if the test data i supplied is not in the train dataset, i should print a message to user that data is not valid instead of telling him the wrong label as 1. 

For instance, in knn classification, i have kneighbours function which tells the distance of my closest neighbours to the test data i supplied in a 0 to 1 scale. So, i could easily eliminate the test data samples which are highly distant from my train data samples by keeping threshold at 0.6 or 0.7. 

Is there any criteria/threshold like this i could do with MLPClassifier or with any one of Incremental Classifiers mentioned here which can restrict my test samples if not present in train dataset ? 

Question migrated from SO
SGDClassifier has desicion_function which tells the distance to the hyperplane, where the values are compared to.

This value could imply too big and too low values.