Support Vector Regression in 6 Steps with Python

Support Vector regression is a type of Support vector machine that supports linear and non-linear regression. As it seems in the below graph, the mission is to fit as many instances as possible between the lines while limiting the margin violations. The violation concept in this example represents as ε (epsilon).

SVR requires the training data:{ X, Y} which covers the domain of interest and is accompanied by solutions on that domain. The work of the SVM is to approximate the function we used to generate the training set to reinforce some of the information we’ve already discussed in a classification problem.

How to Build a Support Vector Regression Model:

4. Train your machine, exactly or approximately, to get contraction coefficient by using the main part of the algorithm.

5. Use this coefficient to create an estimator.

The goal in linear regression is to minimize the error between the prediction and data. In SVR, the goal is to make sure that the errors do not exceed the threshold.

SVR in 6 Steps with Python:

The prediction output is 130002. This prediction is not good enough.

The current problem causes from the unscaled dataset of our practice. Normally several common class types contain the feature scaling function so that they make feature scaling automatically. However, the SVR class is not a commonly used class type so that we should make feature scaling by our codes.

Before coding feature scaling line, restart your kernel the Python IDE. Then put your code in the 3rd step of the code.

Then restart to run the code again. The final SVR graph will be like below. In this model the point on the far upper right side is CEO and that the model takes this value as an outlier.

References:

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