Bagging in Machine Learning

 What Is Ensemble 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.

* 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