Support Vector Machine (SVM)
Used for solving both Classification (via [SVC]) and Regression (via [SVR]) problems.
Support Vector Machine (SVM)
"Margins" or Support Vectors are line parallel to Linear Model Line
The farther the distance [d] here, better the SVM
When there's multiple Linear Model Lines that can be made, SVM allows choosing the line with best fit
SVM Kernels
Take example below, SVM kernels acts on closely looped data points, where dataset is transformed into higher dimension to create separation
SVM Kernels runs transformation on data for better accuracy (better to use with GridSearchCV)
SVC on 2D -> 3D
SVC on 1D -> 2D
SVM Kernels Techniques
Example: Using [Polynomial] Transformation
Before Transformation:
After Transformation:
Then normally, SVC Model training can be run for better accuracy