One-vs-One (OVO) Classifier with Logistic Regression using sklearn in Python

by | Apr 11, 2023 | AI, Machine Learning and Deep Learning, Featured, Machine Learning Using Python, Python Scikit-learn

What is the One-vs-One (OVO) classifier?

A logistic regression classifier is a binary classifier, by default. It can solve a classification problem if the target categorical variable can take two different values. But, we can use logistic regression to solve a multiclass classification problem also. We can use a One-vs-One (OVO) or One-vs-Rest (OVR) classifier along with logistic regression to solve a multiclass classification problem.

As we discussed in our previous articles, a One-vs-One (OVO) classifier breaks a multiclass classification problem into n(n-1)/2 number of binary classification problems, where n is the number of different values the target variable can take. After that, it can use binary classification problems using a binary classifier like a logistic regression classifier. And then, the OVO classifier can use those results to predict the outcome of the target variable.

For example, if the target categorical variable in a multiclass classification problem can take three different values A, B, and C, then the OVO classifier breaks the multiclass classification problem into the following binary classification problem:

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

Now, the One-vs-One (OVO) classifier can solve the binary classification problems with a binary classifier and use the results to predict the outcome of the target variable. (One-vs-Rest vs. One-vs-One Multiclass Classification)

One-vs-One (OVO) Classifier with Logistic Regression using sklearn in Python

We can use the following Python code to implement a One-vs-One (OVO) classifier with logistic regression:

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 first reading the iris dataset using the seaborn Python library. The dataset has four features – sepal length, …

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Amrita Mitra

Author

Ms. Amrita Mitra is an author, who has authored the books “Cryptography And Public Key Infrastructure“, “Web Application Vulnerabilities And Prevention“, “A Guide To Cyber Security” and “Phishing: Detection, Analysis And Prevention“. She is also the founder of Asigosec Technologies, the company that owns The Security Buddy.

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