Why can't I predict new data using SVM and KNN?

Question

I'm new to machine learning and I just learned KNN and SVM with sklearn. How do I make a prediction for new data using SVM or KNN? I have tried both to make prediction. They make good prediction only when the data is already known. But when I try to predict new data, they give an incorrect prediction.

Here is my code:

import numpy as np
from sklearn import svm

x=np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11]], dtype=np.float64)
y=np.array([2,3,4,5,6,7,8,9,10,11,12], dtype=np.float64)

clf = svm.SVC(kernel='linear')
clf.fit(x, y)
print(clf.predict([[20]]))
print(clf.score(x, y))

0utput:

[12.]
1.0

This code will make a good prediction as long as the data to predict is within the range x_train. But when I try to predict for example 20, or anything above the range x_train, the output will always be 12 which is the last element of y. I don't know what I do wrong in the code.

Answer 1

You have to use a regression model rather than a classification model. For svm based regression use svm.SVR()

import numpy as np
from sklearn import svm

x=np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11]], dtype=np.float64)
y=np.array([2,3,4,5,6,7,8,9,10,11,12], dtype=np.float64)

clf = svm.SVR(kernel='linear')
clf.fit(x, y)
print(clf.predict([[50]]))
print(clf.score(x, y))

output:

[50.12]
0.9996