Quick SVC – a classifier and regressor

1. Load iris from the datasets module:

import numpy as np
import pandas as pd
from sklearn import datasets

iris = datasets.load_iris()

1. For simplicity, consider only targets 0 and 1, corresponding to Setosa and Versicolor. Use the Boolean array iris.target < 2 to filter out target 2. Place it within brackets to use it as a filter in defining the observation set X and the target set y:

X = iris.data[iris.target < 2]
y = iris.target[iris.target < 2]

1. Now import train_test_split and apply it:

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state= 7)

1. Prepare and run an SVC by importing it and scoring it with cross-validation:

from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

svc_clf = SVC(kernel = 'linear').fit(X_train, y_train)
svc_scores = cross_val_score(svc_clf, X_train, y_train, cv=4)

1. As done in previous sections, view the average of the scores:

svc_scores.mean()

0.94795321637426899

1. Perform the same with support vector regression by importing SVR from sklearn.svm, the same module that contains SVC:

from sklearn.svm import SVR

1. Then write the necessary syntax to fit the model. It is almost identical to the syntax for SVC, just replacing some c keywords with r:

svr_clf = SVR(kernel = 'linear').fit(X_train, y_train)