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 …






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