Support vector machines (SVM) is one of the techniques we will use that doesn't have an easy probabilistic interpretation. The idea behind SVMs is that we find the plane that separates the group of the dataset the "best". Here, separation means that the choice of the plane maximizes the margin between the closest points on the plane. These points are called support vectors.
SVM is one of my favorite machine learning algorithms. It was one of the first machine learning algorithms I learned in school. So, let's get some data and get started:
>>> from sklearn import datasets >>> X, y = datasets.make_classification()
The mechanics of creating a support vector classifier is very ...