Difference Between Bagging and Random Forest

A particular procedure to solve computational problems is known as an algorithm. There are various types of algorithms.


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In programming, the development of algorithms has a different value than any other technique. A program needs a bunch of best algorithms to run effectively.

Bagging and Random Forest are also two types of algorithms.

Bagging vs Random Forest

The difference between bagging and Random Forest is that bagging is an algorithm that is based on ensemble while the random forest is an algorithm that is an upgraded and enhanced version of bagging, having many features copied from the algorithm bagging. Both algorithms are frequently used in the field of machine learning.

Bagging vs Random Forest

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Bagging is a meta-algorithm that is designed to increase and improve the accuracy and stability of machine learning algorithms that are used in the classification of the terms statistical and regression.

Another name for bagging is bootstrap aggregating. It is a very useful technique to improve a computer program.

Random forest is also an algorithm known as Supervised Machine Learning Algorithm which is also designed to improve the accuracy and stability in the term regression. Programmers use this algorithm widely to solve regression problems.

This technique works by building decision trees for different samples. It also handles datasets that include continuous variables.

Comparison Table

Parameters Of ComparisonBaggingRandom Forest
YearBagging was introduced in the year 1996 almost 2 decades ago. Random forest was introduced. The algorithm, random forest was introduced in the year 2001.
InventorThe bagging algorithm was created by a man named Leo Breiman.After the successful outcome of bagging Leo Breiman created an enhanced version of bootstrap aggregation, random forest.
UsageTo increase the stability of the program, bagging is used by decision trees.The technique random forest is used to solve the problems related to classification and regression.
PurposeThe main purpose of bagging is to train unpruned decision trees belonging to the different sunsets. The main purpose of random forest is to create multiple random trees.
ResultThe bagging algorithm gives the result of a machine learning model with accurate stability.The result given by random forest is the robustness against the overfitting problem in the program.

What is Bagging?

Bagging is an algorithm that is used by many programmers in machine learning. The other name by which bagging is known is bootstrap aggregation.

It is based on ensemble and is a meta-algorithm. Bagging is used in computer programs to increase their accuracy and stability.

The decision tree method has also adapted bagging.

Bagging can be considered as a model averaging approach for special cases. When there’s overfitting in a program and an increase in the number of variances, bagging is used to provide the necessary help to solve these problems.

The number of datasets found in bagging is three, which are bootstrap, original, and out to bag dataset. When the program picks random objects from the dataset, this process leads to the making of a bootstrap database.

In the out to bag dataset, the program represents the remaining objects left in bootstrap.

The bootstrap dataset and out to bag should be created with great attention since they are used to test the accuracy of programs or bagging algorithms.

Multiple decision trees and multiple datasets are generated by bagging algorithms and chances are of an object being left out. To make a tree is used to examine the set of samples that have been bootstrapped.

What is Random Forest?

Random forest is a technique widely used in machine learning programs. It is also known as the Supervised Machine Learning Algorithm.

Random forest takes multiple different samples and builds decision trees so it can solve the problem related to regression and the cases of classification. The majority drawn from the decision trees are used to vote.

When there are continuous variables in classification cases, random forests provide help to handle the dataset. Random forest is known to be an ensemble-based algorithm.

By ensemble one can understand multiple models combined at the same place. There are two methods used by ensembles and bagging is one of them.

The second one is boosting. A collection of decision trees forms a random forest.

When a programmer makes decision trees, he has to make each tree differently to keep diversity between trees.

In a random forest, the space for features is reduced since they are not considered by each tree. The data or attributes used to form every decision tree are different from each other.

The making of random forests uses a CPU thoroughly. There is always a 30% possibility that the entire data will not be used or tested while operating through a random forest.

The results or output depends on the majority provided by decision trees.

Main Differences Between Bagging and Random Forest

  1. Bagging is used when there is no stability found in a machine learning program. While the random forest is used to tackle problems regarding regression.
  2. Bagging sees through the decision trees to check necessary changes and to improve them. On the other hand, random forests create decision trees in the first place.
  3. Bagging was created in 1996 when machine learning was still developing, whereas random forest algorithm was introduced in 2001.
  4. Bagging was developed and improved by Leo Breiman to make machine learning easier and after a year random forest was introduced as an upgraded version also developed by Leo.
  5. Bagging is a meta-algorithm that is based on ensemble technique while the random forest is an enhanced form of bagging.
  1. https://projecteuclid.org/journals/annals-of-statistics/volume-30/issue-4/Analyzing-bagging/10.1214/aos/1031689014.short
  2. https://link.springer.com/chapter/10.1007/978-3-642-31537-4_13
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