Voting Classifier
A Voting Classifier is an ensemble machine learning technique that combines predictions from multiple base models to produce a final output, typically for classification tasks. It aggregates the predictions through majority voting (hard voting) or averaging predicted probabilities (soft voting) to improve accuracy and robustness compared to individual models. This approach leverages the diversity of different algorithms to reduce overfitting and enhance generalization on unseen data.
Developers should use Voting Classifiers when building classification systems where high accuracy and stability are critical, such as in fraud detection, medical diagnosis, or customer churn prediction. It is particularly effective when base models have complementary strengths, as it mitigates individual model biases and errors, leading to better performance in real-world applications with complex datasets.