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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