Ensemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models.
You must have used or come across several of these ensemble learning techniques in your machine learning journey:
- Blending, etc.
These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!
- What is Ensemble Learning?
- Why should you learn Ensemble Learning?
- What are the different types of Ensemble Learning techniques?
- Can you use Ensemble Learning for both Regression and Classification problems?
- What are the most popular Ensemble Learning techniques?
- What’s the intuition behind Bagging in Ensemble Learning?
- Similarly, what’s the idea behind Boosting in Ensemble Learning?
- What’s the difference between Bagging and Boosting?
- Do these Ensemble Learning techniques improve our machine learning model?
- Will learning Ensemble Learning help me crack machine learning interviews and win hackathons?
This course by Analytics Vidhya will introduce you to the concept of ensemble learning and understand the machine learning algorithms that use Ensemble Learning. To cement your understanding of this diverse topic, we will explain the advanced Ensemble Learning techniques in Python using a hands-on case study on a real-life problem!
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.
- Max Voting
- Weighted Averaging
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!
Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals.
- Intuition behind Ensemble Learning
- What is Ensemble Learning?
- What models will be covered in the course?
- Quiz: Introduction to Ensemble Learning
- Max Voting
- Weighted Average
- Quiz: Basic Ensemble Techniques
- Implementing Stacking
- Variants of Stacking
- Bootstrap Sampling
- Quiz: Bootstrap Sampling
- What is Bagging?
- Bagging Meta-Estimator
- Random Forest
- Quiz: Random Forest
- Hyper-parameters of Random Forest
- Quiz: Hyper-parameters of Random Forest
- Implementing Random Forest
- Introduction to boosting
- What is Boosting?
- Quiz: Introduction to Boosting
- Gradient Boosting Algorithm (GBM)
- Math Behind GBM
- Quiz: Gradient Boosting Algorithm
- Extreme Gradient Boosting (XGBoost)
- Implementing XGBoost
- Quiz: XGBoost
- AdaBoost: Adaptive Boosting
- Implementing AdaBoost
- Quiz: AdaBoost
- Next Steps
Who should take the Ensemble Learning and Ensemble Learning Techniques course?
This course is designed for anyone who wants to understand how Ensemble Learning and the various Ensemble Learning techniques work. This is an important concept in machine learning that you would need to have a good grasp on.
I have decent programming experience but no background in machine learning. Is this course right for me?
You should ideally have a basic grasp on machine learning algorithms like decision trees and random forest. We suggest enrolling in our Getting Started with Decision Trees free course for starters.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Ensemble Learning and Ensemble Learning Techniques” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Ensemble Learning and Ensemble Learning Techniques” course in a few hours.
I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Appled Machine Learning” course.
- Can I download the videos in this course?