Modeling-Support Vector Regression (SVR) vs. Linear Regression

Question

I'm a little new with modeling techniques and I'm trying to compare SVR and Linear Regression. I've used f(x) = 5x+10 linear function to generate training and test data set. I've written following code snippet so far:

import csv 
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.linear_model import LinearRegression

with open('test.csv', 'r') as f1:
  train_dataframe = pd.read_csv(f1)

X_train = train_dataframe.iloc[:30,(0)]
y_train = train_dataframe.iloc[:30,(1)]

with open('test.csv','r') as f2:
     test_dataframe = pd.read_csv(f2)

X_test = test_dataframe.iloc[30:,(0)]
y_test = test_dataframe.iloc[30:,(1)]

svr = svm.SVR(kernel="rbf", gamma=0.1)
log = LinearRegression()
svr.fit(X_train.reshape(-1,1),y_train)
log.fit(X_train.reshape(-1,1), y_train)

predSVR = svr.predict(X_test.reshape(-1,1))
predLog = log.predict(X_test.reshape(-1,1))

plt.plot(X_test, y_test, label='true data')
plt.plot(X_test, predSVR, 'co', label='SVR')
plt.plot(X_test, predLog, 'mo', label='LogReg')
plt.legend()
plt.show()

As you can see in the picture, Linear Regression works fine but SVM has poor prediction accuracy.

Please let me know if you any suggestion to tackle this issue.

Thanks

Answer 1

The reason is SVR with kernel rbf don't apply the feature scaling. You need to apply feature scaling before fitting the data to the model.

Sample Code For Feature Scaling

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X = sc_X.fit_transform(X)
sc_y = StandardScaler()
y = sc_y.fit_transform(y)