Using support vector machines for classification tasks

In this recipe, we introduce support vector machines, or SVMs. These powerful models can be used for classification and regression. Here, we illustrate how to use linear and nonlinear SVMs on a simple classification task.

How to do it...

1. Let's import the packages:

   In [1]: import numpy as np
           import pandas as pd
           import sklearn
           import sklearn.datasets as ds
           import sklearn.cross_validation as cv
           import sklearn.grid_search as gs
           import sklearn.svm as svm
           import matplotlib.pyplot as plt
           %matplotlib inline

2. We generate 2D points and assign a binary label according to a linear operation on the coordinates:

   In [2]: X = np.random.randn(200, 2)
           y = X[:, 0] + X[:, 1] > 1

3. We now fit a linear Support Vector Classifier (SVC). This classifier tries to separate the two groups of points with a linear boundary (a line here, but more generally a hyperplane):

   In [3]: # We train the classifier...