We can use the One-vs-Rest (OVR) classifier to solve a multiclass classification problem using a binary classifier. For example, logistic regression or a Support Vector Machine classifier is a binary classifier. We can use an OVR classifier that uses the One-vs-Rest strategy with a binary classifier to solve a multiclass classification problem.
The OVR strategy breaks a multiclass classification problem into n binary classification problems where n is the number of different values that the target categorical variable can take. For example, if the target variable can take three values A, B, and C, then the OVR strategy breaks the problem into the following binary classification problems:
Problem 1: A vs. (B, C) Problem 2: B vs. (A, C) Problem 3: C vs. (A, B)
Now, these binary classification problems can be solved with a binary classifier, and the results can be used by the OVR classifier to predict the outcome of 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 an OVR classifier.
import seaborn from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.multiclass import OneVsRestClassifier 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") ovr = OneVsRestClassifier(classifier) scores = cross_val_score(ovr, 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 contains five columns. The first four …






0 Comments