I am new to Machine Learning 7 I have started following Udacity's Intro to Machine Learning
I was following Simple Vector Machine's when this concept of C and Gamma came along. I did some digging around and found the following:
C - A high C tries to minimize the misclassification of training data and a low value tries to maintain a smooth classification. This makes sense to me.
Gamma - I am unable to understand this one.
Can someone explain this to me in layman terms?
-C parameter: C determines how many data samples are allowed to be placed in different classes. If the value of C is set to a low value, the probability of the outliers is increased, and the general decision boundary is found. If the value of C is set high, the decision boundary is found more carefully.
C is used in the soft margin, which requires understanding of slack variables.
-Soft margin classifier:
-slack variables determine how much margin to adjust.
gamma parameter: gamma determines the distance a single data sample exerts influence. That is, the gamma parameter can be said to adjust the curvature of the decision boundary.
