OneVsOneClassifier and OneVsRestClassifier are two strategies for handling multi-class classification problems. Each approach offers unique benefits and trade-offs depending on the dataset and the problem at hand.
OneVsOneClassifier employs a one-vs-one approach, where a separate binary classifier is trained for each pair of classes. Key hyperparameters include estimator (the base classifier) and n_jobs (the number of jobs to run in parallel).
OneVsRestClassifier, on the other hand, uses a one-vs-rest approach, where a single binary classifier is trained for each class against all other classes. Its key hyperparameters include estimator (the base classifier) and n_jobs (the number of jobs to run in parallel).
The main difference lies in the number of classifiers created: OneVsOneClassifier results in more classifiers but can train each classifier on a smaller subset of data, potentially speeding up individual training times. OneVsRestClassifier results in fewer classifiers but each classifier may need to handle more imbalanced data.
OneVsOneClassifier is advantageous when the number of classes is small and computational resources are sufficient. In contrast, OneVsRestClassifier is preferred for problems with a large number of classes or highly imbalanced datasets.
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score
# Generate synthetic multi-class classification dataset
X, y = make_classification(n_samples=1000, n_classes=3, n_informative=3, n_redundant=0, random_state=42)
# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Fit and evaluate OneVsOneClassifier
ovo = OneVsOneClassifier(LogisticRegression(random_state=42))
ovo.fit(X_train, y_train)
y_pred_ovo = ovo.predict(X_test)
print(f"OneVsOneClassifier accuracy: {accuracy_score(y_test, y_pred_ovo):.3f}")
print(f"OneVsOneClassifier F1 score: {f1_score(y_test, y_pred_ovo, average='weighted'):.3f}")
# Fit and evaluate OneVsRestClassifier
ovr = OneVsRestClassifier(LogisticRegression(random_state=42))
ovr.fit(X_train, y_train)
y_pred_ovr = ovr.predict(X_test)
print(f"\nOneVsRestClassifier accuracy: {accuracy_score(y_test, y_pred_ovr):.3f}")
print(f"OneVsRestClassifier F1 score: {f1_score(y_test, y_pred_ovr, average='weighted'):.3f}")
Running the example gives an output like:
OneVsOneClassifier accuracy: 0.645
OneVsOneClassifier F1 score: 0.641
OneVsRestClassifier accuracy: 0.655
OneVsRestClassifier F1 score: 0.635
- Generate a synthetic multi-class classification dataset using
make_classification. - Split the data into training and test sets using
train_test_split. - Instantiate
OneVsOneClassifierwithLogisticRegressionas the base estimator, fit it on the training data, and evaluate its performance on the test set. - Instantiate
OneVsRestClassifierwithLogisticRegressionas the base estimator, fit it on the training data, and evaluate its performance on the test set. - Compare the test set performance (accuracy and F1 score) of both models.