Fully Explained Voting Ensemble Technique in Machine Learning

Ensemble method for machine learning and data science

Photo by ThisisEngineering RAEng on Unsplash

Ensemble learning in machine learning is a method to use multiple weak learners i.e. different algorithms to create a strong predictive model or strong learners.

In general the types of ensemble methods:

1. Bagging
2. Boosting
3. Voting
4. Stacking

From the above methods, we will study the voting ensemble.

Voting Ensemble

In this technique, we use different machine learning models that will train on the same dataset to make classification or regression predictions.

Assumptions to be taken in voting technique:

1. The base model should be different.
2. The accuracy of each model should be greater than 50%. The final accuracy depends on the prediction probabilities of each model.

As we are using many base models, the effect of poor performance by one algorithm can be managed by the strong performance model.

Types of voting ensemble depend on prediction:

For Classification:

1. Soft voting: The…