Classifiers like logistic regression or Support Vector Machine classifiers are binary classifiers. These classifiers, by default, can solve binary classification problems. But, we can use a One-vs-One (OVO) strategy with a binary classifier to solve a multiclass classification problem, where the target variable can take more than two different values.

A One-vs-One (OVO) classifier uses a One-vs-One strategy to break a multiclass classification problem into several binary classification problems. For example, let’s say the target categorical value of a dataset can take three different values A, B, and C. The OVO classifier can break this multiclass classification problem into the following binary classification problems:

Problem 1: A vs. B
Problem 2: A vs. C
Problem 3: B vs. C

If the target categorical variable of a dataset can take n different values, then the OVO classifier breaks the multiclass classification problem into n(n-1)/2 binary classification problems. Then, each of the subproblems can be solved using a binary classifier. The OVO classifier then uses the results to predict the target variable. (One-vs-Rest vs. One-vs-One Multiclass Classification)

We can use the following Python code to solve a multiclass classification problem using the OVO classifier.

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

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

kfold = KFold(n_splits=10, shuffle=True, random_state=1)
classifier = LogisticRegression(solver="liblinear")
ovo = OneVsOneClassifier(classifier)

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

Here, we are reading the iris dataset using the seaborn Python library. The dataset contains five columns. The first four columns contain the features sepal …