A Simple and Intuitive Guide to Bagging in Machine Learning

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.

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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.

Bagging

What Is Bootstrapping?

Bootstrapping is the method of randomly creating samples of data out of a population with replacement to estimate a population parameter.

Bootstrapping

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

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Bagging Demonstration in Python Using IRIS Dataset

Import the libraries

/LoadLibraries

Load the dataset

LoadDataset

Split the dataset into training and testing

SplitData

Creating sub samples to train models

SubSamples

Define a decision tree

DefineDT

Classification model for bagging

Classification

Train models and print their accuracy

TrainModel

Print the mean accuracy

MeanAccuracy

Display the model’s accuracy 

ModelAccuracy

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.

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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..

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About the Author

Avijeet BiswalAvijeet Biswal

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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