In this article, we will look at the differences between Bagging and Boosting. Bagging and Boosting are two of the most popular machine learning techniques that are part of the overall machine learning methods. They are often better predictive options than standalone machine learning techniques. Both Bagging and Boosting have their pros and cons and if you know how they can help build your model, you can make better decisions.
Let’s look at the details of these one by one, 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 more about Bagging vs Boosting, first understand what bag learning is.
Overall learning is a part of machine learning that combines predictions from multiple models to improve accuracy and performance. Using results from multiple machine learning models, it aims to minimize errors and Prejudices this can occur in individual models.
Ensemble learning methods have three groups of methodologies called Bagging, Boosting and Stacking. These methods ensure better accuracy and less noise, unlike the case of traditional machine learning models.
What is the bagging technique?
Bagging, also known as Bootstrap Aggregating, is all about diversity. This involves training multiple instances of the same learning algorithm on different subsets of training data. Subsets are typically generated by bootstrap sampling, where data points are selected randomly with replacement. Finally, the final prediction is then obtained by averaging the predictions of all individual models for regression problems or voting for classification tasks.
Bagging is responsible for reducing variance through the averaging process, which explains its good performance with high variance models. Additionally, it helps reduce the overfitting scenario, making it ideally suited for noisy and outlier-prone datasets.
Advantages of the bagging technique
- Reducing Variance in Machine Learning Models by training multiple models on different subsets of data, which helps mitigate 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 because the models are independent of each other, making the calculation time efficient.
- A versatile technique that can be applied to different basic learners in fact model independent.
- Stable and reliable final prediction due to the aggregation of predictions from several base models.
Disadvantages of the bagging technique
- Bias is not handled by Bagging in the underlying model. If the base learner is biased, bagging will not correct this problem.
- Sometimes complex and difficult to interpret.
- Resource-intensive, because parallelism involves multiple concurrent working models, can pose a challenge when resources are limited.
Read also: 8 Popular Evaluation Metrics in Machine Learning You Need to Know
What is the boosting technique?
Boosting is another ensemble learning method in machine learning, where weak learners are used to train on the data sequentially, unlike Bagging where parallelism was used. Boosting is more about fine tuning, each subsequent model corrects the mistakes made by its predecessor.
This way, Boosting assigns more weight to misclassified instances, allowing the model to pay special attention to areas where it previously struggled. Continuous attention to misclassified instances helps Boosting create a robust model that can handle complex relationships within the data.
Due to Boosting’s sequential approach, biases in the data are properly handled. It is adaptive and can improve the performance of weak models very well.
Advantages of the boosting technique
- Gives greater accuracy compared to individual weak learners. A more robust and accurate final model is due to the sequential approach focused on error correction.
- Effective at capturing complex relationships in the dataset that are not easily discernible by simpler models.
- The iterative process corrects biases in weak learners. Well suited to tasks where minimizing bias is crucial.
Disadvantages of the boosting technique
- A major disadvantage of Boosting is its computational intensity. Sequential training of models makes it longer.
- Boosting is prone to overfitting, especially when the dataset is noisy or contains outliers.
- Noisy data or instances with incorrect labels can have a significant impact on enhancement performance.
Difference Between Bagging and Boosting
Functionality | Bagging | Booster |
---|---|---|
Objective | Reduce variance by averaging across models | Reduce bias by sequentially correcting errors |
Training process | Parallel training of independent models | Sequential training, correcting errors iteratively |
Overfitting | More resistant thanks to the average | More sensitive, especially in the presence of noise |
Calculation | Efficient thanks to parallelization | More computationally expensive due to sequencing |
Suitability of datasets | Large datasets with high variance | Small to medium datasets with bias and noise |
Popular algorithms | Random Forest | AdaBoost, Gradient Boosting, XGBoost, etc. |
Conclusion
We have discussed the differences between bagging and boosting in this article which I hope you understood. We looked at the definitions of Bagging and Boosting, what their pros and cons are, and then drew a side-by-side differentiation chart. Ensemble learning methods such as Bagging and Boosting sometimes prove to be a very good option to improve performance and predictions.
But ultimately it depends on the underlying dataset and problem requirements that determine what type of model implementation works well.
Learn more:
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 training data. Their predictions are combined to determine the final result.
How is boosting different from bagging?
Boosting is another ensemble learning technique that aims to sequentially correct errors made by weak learners. Unlike bagging, boosting assigns different weights to instances in the training set, with emphasis on misclassified samples, which improves performance and predictions.
What is the basic distinction between bagging and boosting?
Although bagging and boosting involve combining multiple models, the main difference is how they treat these models. On the one hand, Bagging aims to reduce variance by averaging across various models while emphasizing bias reduction 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 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 set of weak learners that can be incrementally improved. It depends on the characteristics of the dataset and the problem at hand.
Can bagging and boosting be combined?
Yes, it is possible to combine bagging and boosting techniques, creating a hybrid package. This is called “boosting bagging”.