In one of our previous articles, we discussed what an Error Correcting Output Code (ECOC) classifier is and how it works. In this article, we will discuss how to implement an Error Correcting Output Code (ECOC) classifier with a Support Vector Machine Classifier (SVC) using sklearn in Python.

We can use the following Python code to implement an Error Correcting Output Code (ECOC) classifier with SVC.

import seaborn
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.svm import SVC
from sklearn.multiclass import OutputCodeClassifier

dataset = seaborn.load_dataset("iris")
D = dataset.values
X = D[:, :-1]
y = D[:, -1]

model = SVC()
ecoc = OutputCodeClassifier(model, code_size=2, random_state=1)

kfold = KFold(n_splits=10, shuffle=True, random_state=1)

scores = cross_val_score(ecoc, X, y, scoring="accuracy", cv=kfold)
print(scores.mean())

Here, we are first reading the iris dataset using the seaborn Python library. Then, we are splitting the columns of the dataset into features and the target variable. …