import addutils.toc ; addutils.toc.js(ipy_notebook=True)
import scipy.io import numpy as np import pandas as pd from addutils import css_notebook css_notebook()
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
A SVM constructs a hyper-plane or set of hyper-planes in a high or infinite dimensional space with the largest distance to the nearest training data points of any class (functional margin). In general, the larger the margin the lower the generalization error of the classifier.
The advantages of support vector machines include:
The disadvantages of support vector machines are:
SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical formulations (see section Mathematical formulation).
LinearSVC is another implementation of Support Vector Classification for the case of a linear kernel.
%matplotlib inline import matplotlib.pyplot as plt
import bokeh.plotting as bk bk.output_notebook()
WARNING:bokeh.resources:Getting CDN URL for local dev version will not produce usable URL