MACHINE LEARNING

Detailed explanation-1: -Parameters are as follows: C: It is the regularization parameter, C, of the error term. kernel: It specifies the kernel type to be used in the algorithm. It can be ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’, or a callable.

Detailed explanation-2: -The most critical hyperparameters for SVM are kernel, C, and gamma . kernel function transforms the training dataset into higher dimensions to make it linearly separable.

Detailed explanation-3: -In summary, model parameters are estimated from data automatically and model hyperparameters are set manually and are used in processes to help estimate model parameters. Model hyperparameters are often referred to as parameters because they are the parts of the machine learning that must be set manually and tuned.

Detailed explanation-4: -The penalty term that is passed as a hyper parameter in SVM while dealing with both linearly separable and non linear solutions is denoted as ‘C’ that is called as Degree of tolerance. Large value of C results in the more penalty SVM gets when it makes a misclassification.