The gamma value, c value and the type of kernel are the hyperparameters of an SVM model.
- C (Cost parameter): It controls the trade-off between having a smooth decision boundary and classifying the training points correctly. A smaller C value makes the decision boundary smoother, and a larger C value aims to classify all training points correctly.
- Kernel: SVM can use different types of kernels to transform the input features into a higher-dimensional space. Common kernels include Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid.
- Gamma: This parameter is specific to the RBF kernel and influences the shape of the decision boundary. A small gamma will create a more general decision boundary, while a large gamma will create a more complex decision boundary that can fit the training data more closely.
- Degree: This parameter is specific to the Polynomial kernel and determines the degree of the polynomial function used to map the input data into a higher-dimensional space.
- Coefficient0: It is the independent term in the kernel function. It is relevant in the Polynomial and Sigmoid kernels.
It’s important to note that the optimal values for these hyperparameters depend on the specific dataset and problem at hand. Tuning these hyperparameters through techniques like grid search or random search is a common practice to find the best combination for a given problem.