What is a voting regressor?

A voting regressor can be defined as a special method that combines or ‘ensembles’ multiple regression models and overperforms the individual models present as its estimators. The mathematical concept of a voting regressor is quite easy and very similar to that of a voting classifier. If we consider a crowd of machine learning models as then each model will produce a prediction for a given input data . Now if we pass it through Voting Regressor then the final prediction will be . Now, we can choose simple average mode which uniformly distributes the total weight to all the models or we can choose custom-specified weights for each model which is called Weighted averaging.

For Simple average:

For Weighted average: where is the custom weights assigned during training process.

But Mathematical expression or concept is not useful for a direct understanding of Voting regressor, so some key facts about it are discussed below:

1. Diverse Models: It’s required to include diverse regression(like traditional ML models along with some other ensemble tree-based models) models in the ensemble. These models can have different strengths and weaknesses which can work a complement each other when passed through a Voting regressor.
2. Simple Averaging vs. Weighted Averaging: The Voting regressor can use simple averaging in which equal weights are assigned for all models or weighted averaging where different weights are assigned to each model’s prediction. The weighted averaging allows us to emphasize the predictions of certain models which are expected to perform better on specific subsets of the data.
3. Training and Prediction: The individual models are trained on the training data and during prediction, each model provides its own prediction which are then combined using the specified aggregation method (averaging) to form the final ensemble prediction. This is the main working principal of Voting Regressor for any kind of datasets or individual models.
4. Performance improvement: The Voting Regressor is often used when we have several candidate models and want to improve overall prediction accuracy by smoothing out the noise introduced by each individual models and provide more stable and accurate predictions.

In the family of ensemble learning, an efficient method for regression tasks in Machine learning is the voting regressor. The voting algorithm has two variants: Voting Classifier and Voting Regressor. The voting classifier is explicitly used for only classification tasks, while the voting regressor is used for regression tasks, but both work in similar ways with few logical changes. This ensemble method combines the predictions from multiple individual regression models (traditional or other ensemble methods) to make a final prediction by leveraging the wisdom of the crowd by simple averaging or weighted averaging of the predictions of its constituent models, which leads to more accurate and robust predictions compared to individual models. In this article, we will discuss the voting regressor and see how we can implement it.