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...
- Let's import the packages:
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
- We generate 2D points and assign a binary label according to a linear operation on the coordinates:
In : X = np.random.randn(200, 2) y = X[:, 0] + X[:, 1] > 1
- We now fit a linear Support Vector Classifier ...