from sklearn.svm import SVR
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import numpy as np
n_samples, n_features = 10, 5
rng = np.random.RandomState(0)
y = rng.randn(n_samples)
X = rng.randn(n_samples, n_features)
regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))
regr.fit(X, y)
#输出
Pipeline(steps=[('standardscaler', StandardScaler()),
('svr', SVR(epsilon=0.2))])
#svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
#svr_rbf.fit(train_x, train_y)
