modelvoting

Model voting, in machine learning, refers to ensemble methods that combine predictions from multiple models to produce a final decision. The idea is that diverse models may make different errors, and aggregating their outputs can improve accuracy, robustness, and generalization across data sets.

Hard voting (majority voting) takes the predicted class labels from each model and selects the class with the most votes. Soft voting averages the predicted class probabilities across models and chooses the class with the highest average probability. Weighted voting assigns a weight to each model, so its vote counts more toward the final decision. Some approaches also use calibration to ensure probabilities are comparable.

Voting is widely used in classification tasks and can be extended to regression by averaging numerical predictions, though the term is most common for classification. Benefits include improved performance on diverse data, robustness to individual model errors, and compatibility with different model types. Drawbacks include potential redundancy when models are highly correlated, sensitivity to poorly calibrated probabilities for soft voting, and the need for careful validation to avoid overfitting.

Implementation considerations include selecting a diverse set of base models, ensuring outputs are compatible (class labels or probability estimates), and managing computational cost. In practice, software libraries often provide voting classifiers that implement hard, soft, and weighted voting. Proper evaluation with cross-validation and out-of-sample testing is important to accurately assess whether voting offers a gain for a given task.