Configure SVC "break_ties" Parameter

The SVC class in scikit-learn is a powerful tool for Support Vector Machine classification. It’s particularly useful when dealing with complex, nonlinear decision boundaries.

The break_ties parameter in SVC determines how to handle cases where multiple classes have equal decision function values. When break_ties is False (default), the class with the smallest index is returned. Setting it to True breaks ties randomly.

This example demonstrates how to configure the break_ties parameter and its effect on model performance, particularly in scenarios with closely situated classes.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Generate synthetic dataset with close classes
X, y = make_classification(n_samples=1000, n_classes=3, n_features=10,
                           n_informative=5, n_redundant=0, n_clusters_per_class=1,
                           class_sep=0.5, 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)

# Train with different break_ties values
for break_ties in [False, True]:
    svc = SVC(kernel='linear', C=1, break_ties=break_ties, random_state=42)
    svc.fit(X_train, y_train)
    y_pred = svc.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    print(f"break_ties={break_ties}, Accuracy: {accuracy:.3f}")

Running the example gives an output like:

break_ties=False, Accuracy: 0.805
break_ties=True, Accuracy: 0.795

The key steps in this example are:

Generate a synthetic multiclass classification dataset with close classes
Split the data into train and test sets
Train SVC models with break_ties set to False and True
Evaluate the accuracy of each model on the test set

Some tips and heuristics for setting break_ties:

Set break_ties to True when you want to introduce randomness to break ties
This can be beneficial when classes are very close and you want to avoid bias towards any particular class

Issues to consider:

The break_ties parameter is only relevant for multiclass classification problems
It only matters when the decision function values are equal for multiple classes

See Also