In this post, you will learn about **SVM RBF (Radial Basis Function) kernel hyperparameters with the python code example. **The following are the two hyperparameters which you need to know while training a machine learning model with SVM and RBF kernel:

• Gamma
• C (also called regularization parameter)

Knowing the concepts on SVM parameters such as Gamma and C used with RBF kernel will enable you to select the appropriate values of Gamma and C and train the most optimal model using the SVM algorithm. Let’s understand why we should use kernel functions such as RBF.

Why Use RBF Kernel?

When the data set is linearly inseparable or in other words, the data set is non-linear, it is recommended to use kernel functions such as RBF. For a linearly separable dataset (linear dataset) one could use linear kernel function (kernel=“linear”). Getting a good understanding of when to use kernel functions will help train the most optimal model using the SVM algorithm. We will use Sklearn Breast Cancer data set to understand SVM RBF kernel concepts in this post. The scatter plot given below represents the fact that the dataset is linearly inseparable and it may be a good idea to apply the kernel method for training the model.

#tutorial #machine learning #ai #rbf kernel