eters and gamma going from left to rightwe increase the value of the parameter gamma from to small gamma means large radius for the gaussian kernelwhich means that many points are considered close by this is reflected in very smooth decision boundaries on the leftand boundaries that focus more on single points further to the right low value of gamma means that the decision boundary will vary slowlywhich yields model of low complexitywhile high value of gamma yields more complex model going from top to bottomwe increase the parameter from to as with the linear modelsa small means very restricted modelwhere each data point can only have very limited influence you can see that at the top left the decision boundary looks nearly linearwith the misclassified points barely having any influence on the line increasing cas shown on the bottom rightallows these points to have stronger influence on the model and makes the decision boundary bend to correctly classify them supervised learning