#8 Machine Learning & Data Science Challenge 8

Bhagirath Deshani Bhagirath Deshani
1 min read

Why Support Vector Regression? Difference between SVR and a simple regression model?

  • In simple regression, try to minimize the error rate. But in SVR, we try to fit the error within a certain threshold.

Concepts:

  1. Boundary

  2. Kernel

  3. Support Vector

  4. Hyper Plane

Blue line: Hyper Plane; Red line: Boundary-Line

  • Our best fit line is the one where the hyperplane has the maximum number of points.

  • We are trying to do here is trying to decide on a decision boundary at e distance from the original hyperplane such that data points closest to the hyperplane or the support vectors are within that boundary line.

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Written by

Bhagirath Deshani
Bhagirath Deshani

Hello everyone! I am Machine Learning Engineer. I am from India. I have been interested in machine learning since my engineering days. I have completed Andrew NG’s original Machine Learning course from Stanford University at Coursera and also completed the IBM course on Machine Learning and Deep Learning. Currently, I am working on Machine Learning and Data Science project. My goal is to use the skills I have acquired to solve real-world problems and make a positive impact on the world.