def test_svm_classification():
y_signed_train = (y_train * 2) - 1
y_signed_test = (y_test * 2) - 1
for kernel in [RBF(gamma=0.1), Linear()]:
model = SVM(max_iter=250, kernel=kernel)
model.fit(X_train, y_signed_train)
predictions = model.predict(X_test)
assert accuracy(y_signed_test, predictions) >= 0.8
def test_svm_classification():
y_signed_train = (y_train * 2) - 1
y_signed_test = (y_test * 2) - 1
for kernel in [RBF(gamma=0.1), Linear()]:
model = SVM(max_iter=250, kernel=kernel)
model.fit(X_train, y_signed_train)
predictions = model.predict(X_test)
assert accuracy(y_signed_test, predictions) >= 0.8
def classification():
X, y = make_classification(n_samples=1200, n_features=10, n_informative=5, random_state=1111, n_classes=2,
class_sep=1.75, )
# Convert y to {-1, 1}
y = (y * 2) - 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1111)
for kernel in [RBF(gamma=0.1), Linear()]:
model = SVM(max_iter=500, kernel=kernel, C=0.6)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('Classification accuracy (%s): %s' % (kernel, accuracy(y_test, predictions)))
def classification():
# Generate a random binary classification problem.
X, y = make_classification(n_samples=1200, n_features=10, n_informative=5,
random_state=1111, n_classes=2, class_sep=1.75,)
# Convert y to {-1, 1}
y = (y * 2) - 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=1111)
for kernel in [RBF(gamma=0.1), Linear()]:
model = SVM(max_iter=500, kernel=kernel, C=0.6)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('Classification accuracy (%s): %s'
% (kernel, accuracy(y_test, predictions)))