What is Machine Learning?
Machine learning, the part of Artificial Intelligence, uses the data and logarithms to do the tasks of humans. Machine Learning is one of the important fields of data science. It mainly focuses on developing computer programs and uses them to learn other things. The main aim of machine learning is to allow computers to learn and do the work without the interference of humans and take further actions accordingly.
Ensemble Methods
Ensemble Methods have the task of obtaining the decisions from the multiple models and combining them to improve the overall performance. You can do this through various techniques and methods. In this article, you will learn about the concept of ensemble methods and their methods. These methods are more efficient and accurate than single models.
Basic Ensemble Techniques
- Max Voting
- Averaging
- Weighted Average
Max Voting
You use this technique in classification problems. Various models are used to make predictions for every single model in this work. These predictions by each of the models are known as the vote. The final prediction is decided by the majority of the predictions made by each modal.
Averaging
This method is similar to the max voting method. The votes are taken similarly by all the models individually. But, the difference comes when you take the average of all the votes obtained by the modals. You use this average mainly in regression problems.
Weighted Average
This technique is the extension of the averaging method. Each of the modals is assigned with the weights, as per the importance of each of the prediction models. AI ML courses can help you to grasp the knowledge of all the major models of the ensemble.
Advanced Ensemble Techniques
After learning and understanding the basic ensemble techniques, let us move further to the advanced ensemble techniques –
Stacking
It is the method of ensemble that collects the predictions from different models and builds the new models. This method of ensembling uses predictions based on a test set.
Blending
This ensembling method is similar to stacking, but it uses only the validation set for making the predictions. To be more clear, blending makes the predictions based on the holdout set only. The holdout set obtained from the blending method builds the modal that runs on the test set.
Conclusion
The primary purpose of machine learning is to find a single model that makes the perfect predictions as per the outcome. Rather than creating a single model and thinking that this model will work the best way, it should make the myriads of predictions and average them to find the single best model. In today’s world, due to the growing technology, the number of applications for ensembling techniques also grows at a considerable speed.
The University of Texas AI and machine learning online course in collaboration with Great Learning can help you understand the ensemble methods and techniques. Therefore, you should sign up for this course and advance your career.
