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.
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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.
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