In our previous article, 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 logistic regression using sklearn in Python.

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

import seaborn
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OutputCodeClassifier

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

model = LogisticRegression()
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 and splitting the columns of the dataset into features and the target variable. The last column of the dataset contains the target variable. So, X here contains all the features and y contains the target variable. …