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.

- 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

- 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

- We now fit a linear
**Support Vector Classifier ...**

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