Using support vector machines for classification tasks

In this recipe, we introduce support vector machines, or SVMs. These models can be used for classification and regression. Here, we illustrate how to use linear and nonlinear SVMs on a simple classification task. This recipe is inspired by an example in the scikit-learn documentation (see http://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html).

How to do it...

1. Let's import the packages:

   >>> import numpy as np
       import pandas as pd
       import sklearn
       import sklearn.datasets as ds
       import sklearn.model_selection as ms
       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:

   >>> 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...