Bootstrapping vs. Boosting

Over the past decade, the field of machine learning has witnessed remarkable advancements in predictive techniques and ensemble learning methods. Ensemble techniques are very popular in machine learning as they increase the concerned model’s accuracy by combining predictions from several machine learning models, following a principle of majority voting.

While there are many ensemble learning methods used in the industry and academia, today we’re going to talk about the two most important of them – Bootstrapping and Boosting. Since both of them are essentially ensemble techniques, they might sound similar to beginners at first but they have a lot of differences when it comes to their approaches.

So today in this article, we will discuss these two methods in detail, along with their advantages and disadvantages, and understand the differences between these two. Understanding these methods will enable you to choose the right one for your use case and make more accurate as well as robust decisions.

What is Bootstrapping?

Bootstrapping can be classified as a statistical technique that involves resampling. The core concept behind bootstrapping is resampling data with replacement, i.e., generating new random datasets from the original dataset using resampling with replacement. This may lead to some datasets being selected more than once and others not being chosen at all. These newly generated samples are then used to train different machine learning models, resulting in ensemble models that make predictions. The predictions gained from these models are then averaged to make a final prediction.

Bootstrapping- Advantages

Utilizing the Bootstrapping method comes with several advantages, some of which are,

  • Reducing Overfitting: Bootstrapping can reduce overfitting by training several models on different variations of the same dataset.
  • Improved Stability: Bootstrapping can enhance the stability of a machine-learning model by reducing variance.

Bootstrapping- Disadvantages

While Bootstrapping comes with advantages, it has some disadvantages too. Some of these are,

  • Computationally Expensive: Bootstrapping can be computationally expensive and complex as it involves training multiple models.
  • Underfitting: It can cause underfitting if the concerned dataset is too small and the resampled datasets need to be more diverse.

What is Boosting?

Boosting is another machine learning ensemble technique whose core concept lies in iteratively improving the predictive performance of a single machine learning model by adjusting the weights of the observations in the training dataset. The basic idea behind boosting is that it covers the mistakes of its previous observations by altering the next iteration and ultimately results in the best possible accurate prediction. As the model improves, the weights are updated again, and this process is repeated until the model reaches a satisfactory level of accuracy or maximum number of iterations. 

The final prediction takes place by combining the predictions of all the models, with each model contributed according to its weighted accuracy.  Boosting can be utilized with various weak models such as neural networks, decision trees, and linear models.

Boosting- Advantages

  • Improved Errors: It can improve the errors made by the previous models, making it more accurate than bootstrapping.
  • Misclassified Samples: By focusing on misclassified samples, boosting can improve imbalanced datasets.
  • Fewer Iterations: It can result in a more precise result in fewer iterations.

Boosting- Disadvantages

While Boosting technique has several advantages, it comes with disadvantages as well. Some of these are,

  • Overfitting: If the number of iterations is too high, boosting can be prone to overfitting.
  • Outliers: Outliers can significantly impact the final model, and boosting can be affected by noise found in the data.

Bootstrapping vs. Boosting- The Difference

Bootstrapping and Boosting are two ensemble learning methods that aim to improve the accuracy of a machine-learning model. However, they have some key differences, which then help to understand in which scenarios they can be utilized to use them to their maximum potential. 

Let’s try to break the key differences down for a better understanding:

  1. Technique:

Bootstrapping creates multiple independent models on different subsets of the concerned data while boosting creates models in a sequential manner based on the outcomes of the previous models, enabling it to learn from its mistakes and make the next models better.

  1. Final Prediction:

Bootstrapping makes the final prediction by averaging the predictions of all the concerned machine learning models, following a democratic approach. On the contrary, boosting makes the final prediction based on all the machine learning models relative to their weights, with relatively more weight given to the models that achieve higher accuracy.

  1. Use Case:

Bootstrapping is best utilized in large datasets with noisy data since it creates multiple subsets from it while boosting is best used on relatively smaller datasets with imbalanced data as it learns from its mistakes and addresses the challenges faced by minority classes in subsequent models.

To be able to fully use both techniques to your advantage according to your use case, it’s important to understand them in detail and know how to apply them to specific problems. If used the right way, you can fully utilize the potential of these ensemble methods and optimize the results of your models.

Bootstrapping vs. Boosting – Comparison Table

Bootstrapping and Boosting are two powerful ensemble learning techniques that can improve the accuracy and robustness of machine learning models. Both approaches have their pros and cons and can be used in diverse scenarios relying on the characteristics of the dataset. Bootstrapping is helpful for large datasets with noisy data and can help reduce overfitting and improve stability. At the same time, Boosting is more effective for small datasets with imbalanced data and can improve accuracy by focusing on misclassified samples. 

Here is a table summarizing the differences between bootstrapping and boosting,

TechniqueMultiple models on different data samplesVarious models on residuals of the previous model
Final PredictionTaking the average of predictions of all the modelsCombining predictions of models with different weights
AdvantagesReduces overfitting.Computationally expensiveMore accurateFaster
DisadvantagesMay lead to underfitting with small datasetsCan be prone to overfitting with high iterations
Useful forUseful for large datasets with noisy dataUseful for small datasets with imbalanced data

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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