#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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Bhagirath Deshani
Bhagirath Deshani

Greetings. I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.