Tuning Support Vector Machine in Python

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The support-vector machine is one of the most popular classification algorithms. The SVM approach to classifying data is elegant, intuitive and includes some very cool mathematics.
SVM also has some hyper-parameters (like what C or gamma values to use) and finding optimal hyper-parameter is a very hard task to solve.
But it can be found by just trying all combinations and see what parameters work best.
I demonstrated how to use GridSearchCV searching method to find optimal hyper-parameters and hence improve the accuracy/prediction results.
  1. Statistics and Risk Modeling
  2. Support Vector Machine
  3. SVM
  4. Optimal plane
  5. kernel trick
  6. Radial Basis Function kernel
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