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).
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
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
We now fit a linear Support Vector Classifier (SVC). This classifier tries to separate the two groups of points with a linear boundary...