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.

Machine Learning Engineers often use “bagging and boosting example” techniques to enhance model performance.

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.
  3. Every model is learned in parallel with the training set. These models are independent of each other.
  4. The final predictions are done by merging the predictions from all the models.

Example of Bagging:

Is Random Forest bagging or boosting? 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.

Advantages of Bagging

  • Reduced Overfitting

One of the primary benefits of bagging, to highlight bagging vs boosting or knowing what’s difference bagging and boosting is its ability to mitigate overfitting. By training models on different subsets of the data, bagging helps prevent individual models from memorizing the training set. This diversity in the models reduces the risk of overfitting, making the overall ensemble more robust and reliable. This can be considered as diff between bagging vs boosting or  boosting vs bagging.

  • Improved Stability and Generalization

Speaking of Bagging or Boosting, or difference between boosting and Bagging enhances the stability and generalization of the model. Since it combines predictions from multiple models, it tends to produce more accurate and consistent results across different datasets. This is particularly beneficial when working with noisy or unpredictable data, as bagging and boosting algorithms helps smooth out irregularities and outliers and what is Bagging in machine learning.

  • Enhanced Model Accuracy

The amalgamation of predictions from various models often leads to a more accurate and reliable final prediction. Each model in the ensemble focuses on different aspects of the data, and their collective wisdom produces a more comprehensive and refined prediction, ultimately boosting the overall accuracy of the model.

  • Robustness to Outliers

Bagging and Boosting mechanism is inherently robust to outliers or anomalies in the data. Outliers can significantly impact the performance of individual models, but by combining predictions from various models, bagging mitigates the influence of these outliers, making the ensemble more resilient and less prone to making biased predictions.

  • Parallelization and Scalability

The bagging process is inherently parallelizable, making it highly efficient and scalable. Each model in the ensemble can be trained independently, allowing for parallel processing. This is especially advantageous when dealing with large datasets, as it enables faster model training and predictions, contributing to overall computational efficiency.

  • Versatility Across Algorithms

Bagging is algorithm-agnostic, meaning it can be applied to various machine learning algorithms without modification. Whether you are working with decision trees, support vector machines, or other models, the bagging technique can be seamlessly integrated, showcasing its versatility and adaptability across different algorithms.

  • Increased Model Robustness

The ensemble created through bagging tends to be more robust and resilient. Even if some models in the ensemble perform poorly in certain instances, the overall impact on the final prediction is mitigated by the majority of well-performing models. This robustness makes bagging particularly suitable for challenging and dynamic real-world scenarios of boosting and bagging in machine learning.l

These are basic of boosting vs bagging in 

Disadvantages of Bagging

  • Increased Computational Complexity

One significant drawback of bagging is its increased computational complexity. Since bagging involves training multiple models on different subsets of the data and combining their predictions, it requires more computational resources and time compared to training a single model and highlighting bagging algorithm in machine learning 

  • Overfitting Potential

While bagging helps lower the variance by averaging predictions from a number of models, it can still lead to overfitting, especially if the base learner used in the ensemble is prone to overfitting. The aggregation of predictions may not effectively mitigate overfitting, particularly when the base models are complex and capture noise in the data.

  • Lack of Interpretability

Another drawback of bagging is its impact on model interpretability. The ensemble model created through bagging tends to be more complex, making it challenging to interpret and understand how individual features contribute to predictions. This lack of interpretability may be undesirable in applications where understanding the underlying factors driving predictions is essential.

  • Limited Improvement for Biased Base Learners

Bagging is most effective when the base learners are diverse and unbiased. However, if the base learners are inherently biased or highly correlated, bagging may not provide significant improvements in predictive performance. In such cases, alternative ensemble methods like boosting or stacking may be more suitable.

  • Sensitivity to Noise

Since bagging involves sampling with replacement from the original dataset to create subsets for training the base models, it can be sensitive to noisy data. Noisy samples may get duplicated across different subsets, leading to an increase in the overall variance of the ensemble predictions.


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.

Advantages of Boosting

  • Improved Accuracy

One of the primary advantages of boosting is its prowess in enhancing model accuracy. By sequentially training multiple weak learners, boosting focuses on correcting errors made by its predecessors. This iterative process ensures that the final model is highly accurate, making it a valuable asset in tasks such as classification and regression. One of the aspect to know is bagging and boosting in machine learning.

  • Handling Complex Relationships

Boosting excels in capturing intricate relationships within the data. Unlike some traditional algorithms that struggle with non-linear patterns, boosting adapts to the complexity of the dataset. It can decipher intricate relationships, making it an ideal choice for scenarios where the underlying patterns are not easily discernible.

  • Robustness to Overfitting

Overfitting, a common concern in machine learning, occurs when a model learns the training data too well but fails to generalize to new, unseen data. Boosting mitigates this risk by emphasizing instances where the model has previously faltered. This results in a more robust model that performs well not only on the training data but also on new, unseen data.

  • Feature Importance and Selection

Boosting provides a natural way to identify and prioritize important features within a dataset. As weak learners are trained sequentially, the algorithm assigns weights to different features based on their contribution to minimizing errors. This inherent feature selection mechanism helps in focusing on the most relevant aspects of the data, streamlining the model and improving efficiency.

  • Versatility Across Domains

The versatility of boosting extends across various domains, from finance to healthcare and beyond. Its adaptability to different types of data and problem domains showcases its wide-ranging applicability. This makes boosting a go-to choice for data scientists and machine learning practitioners working on diverse projects.

  • Mitigation of Bias and Variance

Boosting strikes a delicate balance between bias and variance, two critical aspects in model performance. While weak learners may have high bias, the boosting process progressively reduces bias by emphasizing misclassified instances. Simultaneously, the ensemble nature of boosting helps control variance, preventing the model from being overly sensitive to fluctuations in the training data.

Disadvantages of Boosting

Boosting algorithms, such as AdaBoost and Gradient Boosting, are powerful tools in machine learning for enhancing the performance of predictive models. However, like any technique, they come with their own set of limitations and challenges. Along with highlighting difference between bagging and boosting in data mining.

  • Sensitivity to Noisy Data

Boosting algorithms are highly sensitive to noisy data and outliers. Noisy data refers to data that contains errors or outliers that do not represent the true underlying patterns in the data. Since boosting focuses on correcting misclassifications by assigning higher weights to misclassified instances, noisy data can significantly impact the performance of the algorithm. As a result, boosting models may overfit to the noisy data, leading to poor generalization on unseen data.

  • Computationally Intensive

Another disadvantage of boosting algorithms is their computational complexity. Boosting involves iteratively training multiple weak learners to improve the overall model performance. Each weak learner is trained sequentially, and the subsequent weak learners focus on correcting the errors made by the previous ones. 

This iterative process can be computationally expensive, especially when dealing with large datasets or complex models. As a result, training a boosting model may require considerable computational resources and time.

  • Vulnerability to Overfitting

Despite its ability to reduce bias and variance, boosting is still susceptible to overfitting, especially when the number of boosting iterations is too high. Overfitting occurs when the model captures noise and random fluctuations in the training data rather than the underlying patterns. 

This can lead to poor generalization performance on unseen data. Regularization techniques, such as limiting the maximum depth of individual trees in Gradient Boosting, can help mitigate overfitting, but finding the right balance between bias and variance remains a challenge in

  • Lack of Interpretability

Boosting algorithms often result in complex models that are difficult to interpret. The final boosted model combines multiple weak learners, each of which may use different features and decision boundaries. 

As a result, understanding the inner workings of the model and explaining its predictions to stakeholders or end-users can be challenging. Interpretable models are crucial in domains where transparency and explainability are required, such as healthcare or finance.

  • Data Imbalance

Boosting algorithms may struggle with imbalanced datasets, where one class is significantly more prevalent than the others. Imbalanced datasets can skew the training process, causing the model to focus more on the majority class and neglect the minority class.

This can result in biased predictions, especially for rare events or minority classes. Techniques such as class weighting or resampling can help address imbalanced datasets, but they may not always be effective in boosting algorithms, defining what is Bagging and Boosting.

Difference Between Bagging and Boosting or Bagging vs Boosting 

Feature Bagging Boosting
Objective Reduce variance and prevent overfitting Reduce bias and improve accuracy
Base Learners Independent models trained in parallel Sequentially trained weak learners
Weighting Equal weight for all base learners Weighted based on performance
Error Correction Independent errors; no re-weighting Emphasis on correcting mistakes
Training Speed Parallel training; faster Sequential training; slower
Final Model Average or voting of base models Weighted sum of base learners
Robustness Less prone to overfitting Prone to overfitting if not controlled
Examples Random Forest AdaBoost, Gradient Boosting

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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