Optimizing an SVM

For this example we will continue with the iris dataset, but will use two classes that are harder to tell apart, the Versicolour and Virginica iris species.

In this section we will focus on the following:

• Setting up a scikit-learn pipeline: A chain of transformations with a predictive model at the end
• A grid search: A performance scan of several versions of SVMs with varying parameters

Getting ready

Load two classes and two features of the iris dataset:

#load the libraries we have been using
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn import datasets

iris = datasets.load_iris()
X_w = iris.data[:, :2]  #load the first two features of the iris data 
y_w = iris.target       #load the target of the iris data

X = X_w[y_w != 0]
y = y_w[y_w != 0]

X_1 = X[y == 1]
X_2 = X[y == 2]

How to do it...

1. Begin by splitting the data into training and testing sets:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split...