A voting model is an ensemble model which combines several classifiers but to produce the final result, in case of a classification-based model, takes into account, the classification of a certain data point of all the models and picks the most vouched/voted/generated option from all the given classes in the target column.

In machine learning, a voting model refers to an ensemble learning technique where multiple individual models are trained and their predictions are combined to make a final prediction. The idea is to leverage the diversity of different models to improve overall prediction accuracy and robustness.

There are two main types of voting models:

Hard Voting:
- In hard voting, each individual model in the ensemble “votes” for a class, and the class that receives the majority of votes is chosen as the final prediction.
- This approach is particularly useful when the individual models have different strengths and weaknesses, and combining them can lead to better overall performance.
Soft Voting:
- In soft voting, instead of just counting the votes, the individual models assign a probability to each class, and the average probabilities are used to make the final prediction.
- This is beneficial when the individual models can provide probability estimates, as it takes into account the confidence of each model in its predictions.

The key advantage of voting models is their ability to reduce overfitting and improve generalization by combining the knowledge of multiple models. Common algorithms used for building voting models include decision trees, support vector machines, k-nearest neighbors, and more.