if x1 < x2 then
    class1
else
    class2

import numpy as np
from sklearn.svm import SVC

# Generate 1000 samples with 2 features with random values
X_train = np.random.rand(1000,2)

# Label each sample. If feature "x1" is less than feature "x2" then label as 1, otherwise label is 0.
y_train = X_train[:,0] < X_train[:,1]
y_train = y_train.astype(int) # convert boolean to 0 and 1

svc = SVC(kernel = "rbf", C = 0.9) # tried all kernels and C values from 0.1 to 1.0

svc.fit(X_train, y_train)
print("SVC score: %f" % svc.score(X_train, y_train))

SVC score: 0.992000