Bagging vs Boosting: Ensemble Machine Learning

In this article, we will look at the differences between Bagging vs Boosting. Bagging and Boosting are two of the most popular machine-learning techniques which lie in the ensemble methods in machine learning. They often come under the umbrella of better predictive options than standalone techniques of machine learning. Both Bagging and Boosting have their advantages and disadvantages and if you are aware of ways in which they can contribute to your model building, you can make better decisions.

Let’s look one by one into the details of these, what are bagging and boosting, why do they exist, what are the differences between the two, what purpose do they fulfill, and how?

What is Ensemble Learning?

Before learning in detail about Bagging vs Boosting, first understand what is ensemble learning.

Ensemble learning is a part of machine learning that combines predictions from multiple models in order to improve accuracy and performance. By utilizing the results from multiple machine learning models it aims to minimize the errors and biases that may occur in individual models.

The Ensemble Learning Methods have three groups of methodologies that are known as Bagging, Boosting, and Stacking. These methods ensure better accuracy and less noise, unlike the case of traditional machine learning models.

What is Bagging Technique?

Bagging also known as Bootstrap Aggregating, is all about diversity. It involves training multiple instances of the same learning algorithm on different subsets of the training data. The subsets are typically generated through bootstrap sampling, where data points are randomly selected with replacement. Lastly, the final prediction is then obtained by averaging the predictions of all individual models for regression problems or through voting for classification tasks.

Bagging is responsible for reducing variance by the process of averaging which is the reason behind its good performance with high-variance models. Moreover, it helps in reducing the overfitting scenario which makes it perfectly suitable for datasets that are noisy and outliers prone.

Advantages of Bagging Technique

Reduce Variance in Machine Learning models by training multiple models on different subsets of data which helps in smoothing out the impact of outliers and noise in the data.
Better Generalization Performance since they are exposed to different subsets of data, they are less likely to overfit.
Parallel Processing as the models are independent of each other hence making it computationally time efficient.
Versatile technique that can be applied to various base learners makes it model-agnostic.
Stable and Reliable final prediction due to the aggregation of predictions from multiple base models.

Disadvantages of Bagging Technique

Bias is not handled by Bagging in the underlying model. If the base learner is biased, bagging won’t correct this issue.
Sometimes complex and challenging to interpret.
Resource-Intensive as parallelism has multiple simultaneous working models, may pose a challenge where resources are limited.

Also Read: 8 Popular Evaluation Metrics in Machine Learning You Must Know

What is Boosting Technique?

Boosting is another ensemble learning method in machine learning, where weak learners are used to train on the data in a sequential manner, unlike Bagging where parallelism was being used. Boosting is more about fine-tuning, every subsequent model corrects the errors made by its predecessor.

In this way, Boosting assigns more weight to the misclassified instances which allows the model to pay extra attention to the areas where it previously struggled. The continuous focus on misclassified instances helps Boosting to create a robust model that is capable of handling complex relationships within the data.

Due to the sequential approach in Boosting, bias in data is correctly handled. It is adaptive and can improve the performance of weak models really well.

Advantages of Boosting Technique

Gives higher accuracy compared to individual weak learners. A more robust and precise final model is due to the sequential approach focusing on correcting errors.
Effective in capturing complex relationships in the dataset that are not easily discernible by simpler models.
The iterative process corrects the bias in the weak learners. Well-suited for tasks where minimizing bias is crucial.

Disadvantages of Boosting Technique

One significant drawback of Boosting is its computational intensity. The sequential training of models makes it more time-consuming.
Boosting is prone to overfitting, particularly when the dataset is noisy or contains outliers.
Noisy data, or instances with incorrect labels, can heavily impact Boosting performance.

Difference between Bagging vs Boosting

Feature	Bagging	Boosting
Objective	Reduce variance by averaging over models	Reduce bias by sequentially correcting errors
Training Process	Parallel training of independent models	Sequential training, correcting errors iteratively
Overfitting	More resistant due to averaging	More susceptible, especially in the presence of noise
Computation	Efficient due to parallelization	More computationally expensive due to sequencing
Dataset Suitability	Large datasets with high variance	Small to medium datasets with bias and noise
Popular Algorithms	Random Forest	AdaBoost, Gradient Boosting, XGBoost, etc.

Difference between Bagging vs Boosting in Machine Learning

Conclusion

We discussed the differences between bagging vs boosting in this article which I hope you understood. We looked at the definitions of Bagging and Boosting, what are their advantages and disadvantages, and then drew a side-by-side differentiation table. Ensemble learning methods such as Bagging and Boosting sometimes prove to be a very good option for improved performance and predictions.

But in the end, it depends on the underlying dataset and problem requirements that determine what sort of model implementation works well.

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FAQs

What is Bagging, and How Does it Work?

Bootstrap Aggregating or Bagging is an ensemble learning technique that involves training multiple instances of the same learning algorithm on different subsets of the training data. Their predictions are combined in order to determine the final result.

How does Boosting Differ from Bagging?

Boosting is another ensemble learning technique that aims to correct the errors made by weak learners sequentially. Unlike bagging, boosting assigns different weights to instances in the training set putting the emphasis on misclassified samples which improves performance and predictions.

What’s the fundamental distinction between bagging and boosting?

While both bagging and boosting involve combining multiple models, the primary difference lies in how they treat these models. On one hand, Bagging aims to reduce variance by averaging over diverse models while boosting focuses on reducing bias by giving more weight to misclassified instances.

What Are Some Examples of Bagging Algorithms?

Random Forest is a widely known Bagging Algorithm that uses multiple decision trees for decision-making. These trees are trained on different subsets of the data and collectively contribute to the final prediction.

When to Use Bagging or Boosting?

Bagging is generally robust and works well when the base learner is sensitive to noise while boosting excels in scenarios where you have a collection of weak learners that can be progressively improved. It depends on the characteristics of the dataset and the problem at hand.