What are Voting Ensembles?

A voting ensemble (or a “ majority voting ensemble “) is an ensemble machine learning model that combines the predictions from multiple other models.

It is a technique that may be used to improve model performance, ideally achieving better performance than any single model used in the ensemble.

A voting ensemble works by combining the predictions from multiple models. It can be used for classification or regression. In the case of regression, this involves calculating the average of the predictions from the models. In the case of classification, the predictions for each label are summed and the label with the majority vote is predicted.

  • Regression Voting Ensemble : Predictions are the average of contributing models.
  • Classification Voting Ensemble : Predictions are the majority vote of contributing models.

There are two approaches to the majority vote prediction for classification; they are hard voting and soft voting.

Hard voting involves summing the predictions for each class label and predicting the class label with the most votes. Soft voting involves summing the predicted probabilities (or probability-like scores) for each class label and predicting the class label with the largest probability.

  • Hard Voting . Predict the class with the largest sum of votes from models
  • Soft Voting . Predict the class with the largest summed probability from models.

A voting ensemble may be considered a meta-model, a model of models.

As a meta-model, it could be used with any collection of existing trained machine learning models and the existing models do not need to be aware that they are being used in the ensemble. This means you could explore using a voting ensemble on any set or subset of fit models for your predictive modeling task.

A voting ensemble is appropriate when you have two or more models that perform well on a predictive modeling task. The models used in the ensemble must mostly agree with their predictions.