# Support Vector Regression (SVR) using linear and non-linear kernels¶

Toy example of 1D regression using linear, polynomial and RBF kernels.

import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt


## Generate sample data¶

X = np.sort(5 * np.random.rand(40, 1), axis=0)
y = np.sin(X).ravel()

# add noise to targets
y[::5] += 3 * (0.5 - np.random.rand(8))


## Fit regression model¶

svr_rbf = SVR(kernel="rbf", C=100, gamma=0.1, epsilon=0.1)
svr_lin = SVR(kernel="linear", C=100, gamma="auto")
svr_poly = SVR(kernel="poly", C=100, gamma="auto", degree=3, epsilon=0.1, coef0=1)


## Look at the results¶

lw = 2

svrs = [svr_rbf, svr_lin, svr_poly]
kernel_label = ["RBF", "Linear", "Polynomial"]
model_color = ["m", "c", "g"]

fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)
for ix, svr in enumerate(svrs):
axes[ix].plot(
X,
svr.fit(X, y).predict(X),
color=model_color[ix],
lw=lw,
label="{} model".format(kernel_label[ix]),
)
axes[ix].scatter(
X[svr.support_],
y[svr.support_],
facecolor="none",
edgecolor=model_color[ix],
s=50,
label="{} support vectors".format(kernel_label[ix]),
)
axes[ix].scatter(
X[np.setdiff1d(np.arange(len(X)), svr.support_)],
y[np.setdiff1d(np.arange(len(X)), svr.support_)],
facecolor="none",
edgecolor="k",
s=50,
label="other training data",
)
axes[ix].legend(
loc="upper center",
bbox_to_anchor=(0.5, 1.1),
ncol=1,
fancybox=True,