What is the purpose of the 'gamma' parameter in the RBF kernel of SVM?
Practice Questions
Q1
What is the purpose of the 'gamma' parameter in the RBF kernel of SVM?
To control the width of the margin
To define the influence of a single training example
To adjust the number of support vectors
To increase the dimensionality of the data
Questions & Step-by-Step Solutions
What is the purpose of the 'gamma' parameter in the RBF kernel of SVM?
Step 1: Understand that SVM stands for Support Vector Machine, which is a type of machine learning model used for classification tasks.
Step 2: Know that the RBF kernel (Radial Basis Function kernel) is a way to transform data into a higher dimension to make it easier to classify.
Step 3: Learn that the 'gamma' parameter is a setting in the RBF kernel that determines how much influence a single training example has on the model.
Step 4: Realize that a low 'gamma' value means that each training example has a far-reaching influence, leading to a smoother decision boundary.
Step 5: Understand that a high 'gamma' value means that each training example has a close influence, resulting in a more complex and wiggly decision boundary.
