Helpful examples of using Voting machine learning algorithms in scikit-learn.
The Voting algorithm is an ensemble learning method used for classification and regression tasks that combines the predictions from multiple models to make a final decision.
In classification, there are two types of voting: hard voting and soft voting. Hard voting involves taking a majority vote from all the base models, where the class with the most votes is the final prediction. Soft voting, on the other hand, averages the predicted probabilities of each class from all models and selects the class with the highest average probability.
In regression, the final prediction is typically the average (mean) of the predictions made by the individual models.
Voting algorithms are straightforward to implement and can improve overall model performance by leveraging the strengths of diverse models, reducing the risk of overfitting compared to using a single model. However, the effectiveness of the voting algorithm depends on the diversity and performance of the individual models included in the ensemble.