SVM Series
A series of notebooks on Support Vector Machine algorithm
Notebook 1 : Notebook on Support Vector Machine(SVM) Geometric intuition : which is a better classifying plane / line ?
Notebook 2 : Notebook on applying SVM on the Indian Diabetes Dataset to find the best value of C for which accuracy is the highest.
Notebook 3 : Notebook to visualize the decision boundary hyperplane of SVM algorithm, for both linearly separable and non-linearly separable data.
LINEARLY SEPARABLE SAMPLE DATA :
NON-LINEARLY SEPARABLE SAMPLE DATA :
- Linear kernel
- Polynomial kernel with default degree = 3
- Polynomial kernel with degree = 2
- Rbf kernel
- Gaussian RBF kernel in 3D space
Notebook 4 : Notebook to find out the best hyperparameters for a SVC working on the very popular Breast Cancer dataset