What is a One-Vs-One (OVO) classifier?
A Support Vector Machine Classifier (SVC) is a binary classifier. It can solve a classification problem if the target categorical variable can take only two different values. But, we can use a One-Vs-One (OVO) or One-Vs-Rest (OVR) classifier along with SVC to solve a multiclass classification problem, where the target categorical variable can take more than two different values.
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. For example, if the target variable can take three different values A, B, and C, then an OVO classifier will break the problem into the following binary classification problems:
Problem 1: A vs. B Problem 2: A vs. C Problem 3: B vs. C
Now, the OVO classifier can use a binary classifier to solve these binary classification problems and then, 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 Suuport Vector Machine Classifier (SVC) using sklearn in Python
We can use the following Python code to solve a multiclass classification problem using an OVO classifier with SVC.
import seaborn from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.multiclass import OneVsOneClassifier from sklearn.svm import SVC 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 = SVC() 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 five columns. The first four columns contain the features sepal length, sepal width, petal length, and petal width. The last column contains the target variable species…






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