Machine Learning uses several techniques to build models and improve their performance. Ensemble learning methods help improve the accuracy of classification and regression models. This article will discuss one of the most popular ensemble learning algorithms, i.e., Bagging in Machine Learning.
What Is Ensemble Learning?
Ensemble learning is a widely-used and preferred machine learning technique in which multiple individual models, often called base models, are combined to produce an effective optimal prediction model. The Random Forest algorithm is an example of ensemble learning.
What Is Bagging in Machine Learning?
Bagging, also known as Bootstrap aggregating, is an ensemble learning technique that helps to improve the performance and accuracy of machine learning algorithms. It is used to deal with bias-variance trade-offs and reduces the variance of a prediction model. Bagging avoids overfitting of data and is used for both regression and classification models, specifically for decision tree algorithms.
What Is Bootstrapping?
Bootstrapping is the method of randomly creating samples of data out of a population with replacement to estimate a population parameter.
Steps to Perform Bagging
- Consider there are n observations and m features in the training set. You need to select a random sample from the training dataset without replacement
- A subset of m features is chosen randomly to create a model using sample observations
- The feature offering the best split out of the lot is used to split the nodes
- The tree is grown, so you have the best root nodes
- The above steps are repeated n times. It aggregates the output of individual decision trees to give the best prediction
Advantages of Bagging in Machine Learning
- Bagging minimizes the overfitting of data
- It improves the model’s accuracy
- It deals with higher dimensional data efficiently
Bagging Demonstration in Python Using IRIS Dataset
Import the libraries
Load the dataset
Split the dataset into training and testing
Creating sub samples to train models
Define a decision tree
Classification model for bagging
Train models and print their accuracy
Print the mean accuracy
Display the model’s accuracy
From the above demonstration, you can conclude that the individual models (weak learners) overfit the data and have a high variance. But the aggregated result has a reduced variance and is trustworthy.
Choose the Right Program
Supercharge your career in AI and ML with Simplilearn's comprehensive courses. Gain the skills and knowledge to transform industries and unleash your true potential. Enroll now and unlock limitless possibilities!
Program Name
AI Engineer
Post Graduate Program In Artificial Intelligence
Post Graduate Program In Artificial Intelligence
Geo All Geos All Geos IN/ROW University Simplilearn Purdue Caltech Course Duration 11 Months 11 Months 11 Months Coding Experience Required Basic Basic No Skills You Will Learn 10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more. 16+ skills including
chatbots, NLP, Python, Keras and more.8+ skills including
Supervised & Unsupervised Learning
Deep Learning
Data Visualization, and more.Additional Benefits Get access to exclusive Hackathons, Masterclasses and Ask-Me-Anything sessions by IBM
Applied learning via 3 Capstone and 12 Industry-relevant ProjectsPurdue Alumni Association Membership Free IIMJobs Pro-Membership of 6 months Resume Building Assistance Upto 14 CEU Credits Caltech CTME Circle Membership Cost $$ $$$$ $$$$ Explore Program Explore Program Explore Program
Conclusion
Bagging is a crucial concept in statistics and machine learning that helps to avoid overfitting of data. It is a model averaging procedure that is often used with decision trees but can also be applied to other algorithms.
We hope this article helped you understand the importance of bagging in machine learning. Do you have any questions related to this article? If you do, feel free to share the questions with us by placing them in the comments section of this page, below. Our team would be happy to review and help you out with resolutions as soon as possible.
If you’re looking for a course that covers everything from the fundamentals to advanced techniques like machine learning algorithm development and unsupervised learning, look no further than Simplilearn’s comprehensive AI and ML Certification training or Caltech Machine Learning Bootcamp.