Hands-on Supervised Learning with Python: Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms Using Python (English Edition) by Gnana Lakshmi T C & Madeleine Shang

Author:Gnana Lakshmi T C & Madeleine Shang [T C, Gnana Lakshmi & Shang, Madeleine]
Language: spa
Format: epub
ISBN: 9789389328974
Publisher: 2021
Published: 2021-01-15T00:00:00+00:00

Conclusion

For data with multiple dimensions, support vector machines are high algorithms to work with. Different types of kernels help us define the different types of hyperplanes, depending on the way the classes are separated. SVMs give us the ability to define hyperplanes for various distributions like linear, polynomial, and radial distributions.

There are several pros of using SVMs, some of which are:

If the number of attributes is much higher

Best fit if the classes are separable

Depends on support vectors; therefore, outliers are not affected too much

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