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