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 ...

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