Multiclass classification with SVM

We begin expanding the previous recipe to classify all iris flower types based on two features. This is not a binary classification problem, but a multiclass classification problem. These steps expand on the previous recipe.

Getting ready

The SVC classifier (scikit's SVC) can be changed slightly in the case of multiclass classifications. For this, we will use all three classes of the iris dataset.

Load two features for each class:

#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 = iris.data[:, :2]  #load the first two features of the iris data 
y = iris.target       #load the target of the iris data

X_0 = X[y == 0]
X_1 = X[y == 1]
X_2 = X[y == 2]

Split 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(X, y, test_size=0.2, random_state=7,stratify...