Bagging vs Boosting in Machine Learning: Difference Between Bagging and Boosting

Owing to the proliferation of Machine learning applications and an increase in computing power, data scientists have inherently implemented algorithms to the data sets. The key to which an algorithm is implemented is the way bias and variance are produced. Models with low bias are generally preferred.

Organizations use supervised machine learning techniques such as decision trees to make better decisions and generate more profits. Different decision trees, when combined, make ensemble methods and deliver predictive results.

The main purpose of using an ensemble model is to group a set of weak learners and form a strong learner. The way it is done is defined in the two techniques: Bagging and Boosting that work differently and are used interchangeably for obtaining better outcomes with high precision and accuracy and fewer errors. With ensemble methods, multiple models are brought together to produce a powerful model.

This blog post will introduce various concepts of ensemble learning. First, understanding the ensemble method will open pathways to learning-related methods and designing adapted solutions. Further, we will discuss the extended concepts of Bagging and Boosting for a clear idea to the readers about how these two methods differ, their basic applications, and the predictive results obtained from both.

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What is an Ensemble Method?

The ensemble is a method used in the machine learning algorithm. In this method, multiple models or ‘weak learners’ are trained to rectify the same problem and integrated to gain desired results. Weak models combined rightly give accurate models.

First, the base models are needed to set up an ensemble learning method that will be clustered afterward. In the Bagging and Boosting algorithms, a single base learning algorithm is used. The reason behind this is that we will have homogeneous weak learners at hand, which will be trained in different ways.

The ensemble model made this way will eventually be called a homogenous model. But the story doesn’t end here. There are some methods in which different types of base learning algorithms are also implied with heterogeneous weak learners making a ‘heterogeneous ensemble model.’ But in this blog, we will only deal with the former ensemble model and discuss the two most popular ensemble methods herewith.

  1. Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
  2. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.

That was Bagging and Boosting at a glimpse. Let’s look at both of them in detail. Some of the factors that cause errors in learning are noise, bias, and variance. The ensemble method is applied to reduce these factors resulting in the stability and accuracy of the result.

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Bagging is an acronym for ‘Bootstrap Aggregation’ and is used to decrease the variance in the prediction model. Bagging is a parallel method that fits different, considered learners independently from each other, making it possible to train them simultaneously.

Bagging generates additional data for training from the dataset. This is achieved by random sampling with replacement from the original dataset. Sampling with replacement may repeat some observations in each new training data set. Every element in Bagging is equally probable for appearing in a new dataset. 

These multi datasets are used to train multiple models in parallel. The average of all the predictions from different ensemble models is calculated. The majority vote gained from the voting mechanism is considered when classification is made. Bagging decreases the variance and tunes the prediction to an expected outcome.

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Suppose you have a set D of d tuples. At every iteration i, a training set Di of the d tuples is chosen through row sampling using a replacement method from D. Subsequently, a classifier model Mi is learned for every training set D < i. Every classifier Mi provides its class prediction. Also, the bagged classifier M* calculates the votes and allocates the class with the highest votes to X (unidentified sample). This example of bagging in machine learning gives you an idea of how bagging work.

Implementation steps:

You can implement bagging in machine learning by following these steps.

  1. Multiple subsets are prepared from the original data set with equal tuples. The observations with replacement are selected.
  2. A base model is prepared on every subset.

iii. Every model is learned in parallel with the training set. These models are independent of each other.

  1. The final predictions are done by merging the predictions from all the models.

Example of Bagging:

The Random Forest model uses Bagging, where decision tree models with higher variance are present. It makes random feature selection to grow trees. Several random trees make a Random Forest.

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The steps to implement a Random forest:

  • Consider X observations and Y features in the training data set.
  • Firstly, a model from the training data set is randomly chosen with substitution.
  • In this step, the tree is grown to the largest.
  • The above steps are repeated and the prediction is provided. The prediction depends on the collection of predictions from the ‘n’ number of trees.

 Pros of using the Random Forest technique:

  • It efficiently manages a higher-dimension data set.
  • It handles missing quantities and maintains high accuracy for missing data.

 Cons of using the Random Forest technique:

  • The last prediction depends on the mean predictions from the subset trees, so it will not provide an accurate value for the regression model.

 You can easily understand that bagging is the example of which type of learning after understanding this example and its steps.


Boosting is a sequential ensemble method that iteratively adjusts the weight of observation as per the last classification. If an observation is incorrectly classified, it increases the weight of that observation. The term ‘Boosting’ in a layman language, refers to algorithms that convert a weak learner to a stronger one. It decreases the bias error and builds strong predictive models.

Data points mispredicted in each iteration are spotted, and their weights are increased. The Boosting algorithm allocates weights to each resulting model during training. A learner with good training data prediction results will be assigned a higher weight. When evaluating a new learner, Boosting keeps track of learner’s errors. 

If a provided input is inappropriate, its weight is increased. The purpose behind this is that the forthcoming hypothesis is more likely to properly categorize it by combining the entire set, at last, to transform weak learners into superior performing models.

It involves several boosting algorithms. The original algorithms invented by Yoav Freund and Robert Schapire were not adaptive. They couldn’t make the most of the weak learners. These people then invented AdaBoost, which is an adaptive boosting algorithm. It received the esteemed Gödel Prize and was the first successful boosting algorithm created for binary classification. AdaBoost stands for Adaptive Boosting. It merges multiple “weak classifiers” into a “strong classifier”.

Gradient Boosting represents an extension of the boosting procedure. It equates to the combination of Gradient Descent and Boosting. It uses a gradient descent algorithm capable of optimizing any differentiable loss function. Its working involves the construction of an ensemble of trees, and individual trees are summed sequentially. The subsequent tree restores the loss (the difference between real and predicted values).

Just like the algorithm of bagging in machine learning, Boosting involves an algorithm with the following steps.

Implementation steps of a Boosting algorithm:

  1. Initialize the dataset and allocate equal weight to every data point.
  2. Offer this as input to the model and detect the incorrectly classified data points.

iii. Increase the incorrectly classified data points’ weights and decrease the correctly classified data points’ weights.

  1. Normalize the weights of each data point.

Understanding the working of boosting and bagging in ML helps you to effectively carry out comparison between them. So, let’s understand how Boosting works.

How does Boosting work?

The following steps are involved in the boosting technique:

  1. A subset wherein every data point is provided equal weights is prepared from the training dataset.
  2. This step prepares a based model is created for the initial dataset. This model helps you to perform predictions on the whole dataset.

iii. Errors are counted using actual and predicted values. The observation that was incorrectly predicted is provided a higher weight.

  1. Boosting algorithm tries to correct the previous model’s errors.
  2. The process is iterated for multiple models and each of them corrects the previous model’s errors.
  3. The final model works as a strong learner and shows the weighted mean of all the models.

Example of Boosting: 

The AdaBoost uses Boosting techniques, where a 50% less error is required to maintain the model. Here, Boosting can keep or discard a single learner. Otherwise, the iteration is repeated until achieving a better learner.

Similarities and Differences between Bagging and Boosting

Bagging and Boosting, both being the popularly used methods, have a universal similarity of being classified as ensemble methods. Here we will highlight more similarities between them, followed by the differences they have from each other. Let us first start with similarities as understanding these will make understanding the differences easier.

Bagging and Boosting: Similarities

  1. Bagging and Boosting are ensemble methods focused on getting N learners from a single learner.
  2. Bagging and Boosting make random sampling and generate several training data sets 
  3. Bagging and Boosting arrive upon the end decision by making an average of N learners or taking the voting rank done by most of them.
  4. Bagging and Boosting reduce variance and provide higher stability with minimizing errors.

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Bagging and Boosting: Differences

As we said already,

Bagging is a method of merging the same type of predictions. Boosting is a method of merging different types of predictions.

Bagging decreases variance, not bias, and solves over-fitting issues in a model. Boosting decreases bias, not variance.

In Bagging, each model receives an equal weight. In Boosting, models are weighed based on their performance.

Models are built independently in Bagging. New models are affected by a previously built model’s performance in Boosting.

In Bagging, training data subsets are drawn randomly with a replacement for the training dataset. In Boosting, every new subset comprises the elements that were misclassified by previous models.

Bagging is usually applied where the classifier is unstable and has a high variance. Boosting is usually applied where the classifier is stable and simple and has high bias.

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How does Bagging and Boosting obtain N learners?

Bagging and Boosting obtain N learners by creating additional data in the training stage. The random sampling and substitution from the original set produce N new training data sets. The replacement and sampling mean that certain observations may be iterated in every new training data set.

Any element has the same probability to exist in a new data set in bagging in ML. The observations are weighted in Boosting. Thus, some of them will frequently take part in the new sets.

 Which one to use -Bagging or Boosting?

Both of them are useful for data science enthusiasts to solve any classification problem. The choice among these two depends on the data, the circumstances, and the simulation. Moreover, the choice of the ensemble technique is simplified as you gain more experience working with them.

Boosting and Bagging techniques reduce the variance of your single estimate. This is because they merge several estimates from various models. Hence, the result might show a model with improved stability.

Bagging is preferable when the classifier is not robust and shows high variance. You can understand bagging is the example of which type of learning when you start implementing it. But if the classifier features a high bias, then Boosting will provide the desired results.

The bagging in ML will seldom provide a better bias if using a single model shows a low performance. On the other hand, Boosting can create a combined model with a lower error rate because it corrects the weights of incorrectly predicted data points.

Bagging must be considered if a single model’s downfall overfits the training data. The reason is boosting doesn’t prevent overfitting data. Therefore, Bagging is more effective and the preferred choice for most data scientists.

You should choose a base learner algorithm to use Boosting or Bagging. For instance, if you select a classification tree, Bagging and Boosting will comprise a pool of trees as large as you want.

Bagging and Boosting: A Conclusive Summary

Now that we have thoroughly described the concepts of Bagging and Boosting, we have arrived at the end of the article and can conclude how both are equally important in Data Science and where to be applied in a model depends on the sets of data given, their simulation and the given circumstances. Thus, on the one hand, in a Random Forest model, Bagging is used, and the AdaBoost model implies the Boosting algorithm.

A machine learning model’s performance is calculated by comparing its training accuracy with validation accuracy, which is achieved by splitting the data into two sets: the training set and validation set. The training set is used to train the model, and the validation set is used for evaluation. 

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Why is bagging better than boosting?

From the dataset, bagging creates extra data for training. Random sampling and substitution from the original dataset is used to achieve this. In each new training data set, sampling with replacement may repeat certain observations. Every Bagging element has the same chance of emerging in a fresh dataset. Multiple models are trained in parallel using these multi datasets. It is the average of all the forecasts from several ensemble models. When determining classification, the majority vote obtained through the voting process is taken into account. Bagging reduces variation and fine-tunes the prediction to a desired result.

How are the main differences bagging and boosting?

Bagging is a technique for reducing prediction variance by producing additional data for training from a dataset by combining repetitions with combinations to create multi-sets of the original data. Boosting is an iterative strategy for adjusting an observation's weight based on the previous classification. It attempts to increase the weight of an observation if it was erroneously categorized. Boosting creates good predictive models in general.

What are the similarities between bagging and boosting?

Bagging and boosting are ensemble strategies that aim to produce N learners from a single learner. They sample at random and create many training data sets. They arrive at their final decision by averaging N learners' votes or selecting the voting rank of the majority of them. They reduce variance and increase stability while reducing errors.

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