Ensemble Learning and Ensemble Learning Techniques

Just like you come to a decision to buy a car by reading multiple reviews and opinions, in machine learning also, you can combine the decisions from multiple models to improve the overall performance. This technique of combining multiple machine learning models is called ensemble learning.

 

Why do we need to know about Ensemble Learning?

Ensemble learning is one of the most effective ways to build an efficient machine learning model. You can build an ensemble machine learning model using simple models and yet get great scores which are at par with the resource-hungry models like neural networks.

 

What are the different types of Ensemble Learning techniques?

There are simple and advanced ensemble learning techniques.

1. Simple:
   1. Max Voting
   2. Averaging
   3. Weighted Averaging
2. Advanced
   1. Stacking
   2. Blending
   3. Bagging
   4. Boosting

In bagging and Boosting, there are other popular models like Gradient Boosting, Random Forest, XGBoost, etc.

We will be covering all these techniques comprehensively and with Python code in this course.

 

Do we use Ensemble Learning techniques only for classification or regression or both?

We can use ensemble learning for both types of machine learning problems: Classification and Regression. While techniques like Max Voting are used for classification, techniques like Random Forest, Gradient Boosting, etc can be used for both Classification and Regression problems.

 

What are the more popular ensemble learning techniques?

Ensemble learning techniques like Random Forest, Gradient Boosting and variants, XGBoost, and LightGBM are extremely popular in hackathons. Depending on the data we are dealing with, we can use these techniques as our machine learning models. For example, you can use LightGBM(Light Gradient Boosting) for large datasets, or CatBoost for when your data has categorical variables.

 

What is the intuition behind Bagging?

The idea behind bagging is combining the results of multiple models (for instance, all decision trees) to get a generalized result. Bagging (or Bootstrap Aggregating) technique uses subsets (bags) to get a fair idea of the distribution (complete set).

What is the intuition behind Boosting?

Boosting is a sequential process, where each subsequent model attempts to correct the errors of the previous model on subsets of the data. The succeeding models are dependent on the previous model. The boosting algorithm combines a number of weak learners to form a strong learner and boost your overall results.

 

What is the difference between Bagging and Boosting?

While both bagging and boosting involve creating subsets, bagging makes these subsets randomly, while boosting prioritizes misclassified subsets. Additionally, at the final step in bagging, the weighted average is used, while boosting uses majority weighted voting.

 

Does ensemble learning improve my machine learning model?

In a word, yes! And that too drastically! Ensemble learn can improve the results of your machine learning even exponentially at times. There are two major benefits of Ensemble models:

• More accurate predictions(closer to the actual value)
• Combining multiple simple models to make a strong model improves the stability of the overall machine learning model.

As you will learn in the course, we will take a real-life dataset and study how ensemble learning improves the score of our machine learning model as compared to using only simple models.

 

Would knowing about ensemble learning help me crack interviews and hackathons?

Ensemble learning is the go-to method to achieve a high rank on hackathon leaderboards. You can go over the winning approaches of multiple hackathons, and there is a guarantee that a majority would have used an ensemble technique as their machine learning model.

Not only in hackathons but ensemble models are also used extremely popular in the industry because of how cost-effective they are. That is why, questions on random forest, gradient boosting, stacking, etc are often asked in interviews. Proposing an ensemble learning solution for a problem statement in an interview would always give you an edge over other solutions!

 

Who is the Ensemble Learning and Ensemble Learning Techniques Course for?

This course is designed for anyone who:

• Wants to learn about Ensemble Learning in Machine Learning
• Wants to expand their current machine learning skillset
• Is a newcomer to Machine Learning
• Is looking to ace machine learning hackathons
• Is passionate about machine learning!