The future will be drastically changed by machine learning, and the models that it employs for prediction and decision-making are excellent. Ensemble approaches in machine learning combine several learning algorithms to provide predictions that are more accurate than those produced by any one of the individual learning algorithms working alone. By merging many models, ensemble learning enhances the performance of machine learning models. When compared to a single model, this method produces greater predictive performance.
In machine learning, two techniques for ensemble learning are bagging and boosting. Similar to each other, bagging and boosting are ensemble approaches that combine a group of weak learners to produce a strong learner that outperforms a single one. In this article, we will learn the difference between Bagging and Boosting in Machine learning, when to use each, real-life applications, and much more.
What is Bagging?
Using bootstrapped data sets to train an ensemble is known as bootstrap aggregation or bagging. Put simply, it’s the technique of ensemble learning that’s frequently employed to lower variance in a noisy data collection. By choosing from the original training data set with replacement, a bootstrapped set is produced. Consequently, a given example may appear zero, one, or more than once in a bootstrap set.
Applying the Bootstrap process to a high-variance machine learning algorithm – usually decision trees – is known as bagging. Bagging’s main concept is to lessen overfitting by merging predictions from several models, each of which has undergone separate training. Bagging’s main goal is to reduce variance, which strengthens and stabilises models when they are used with unknown data.
Working of Bagging
Bagging or bootstrap aggregation in machine learning works as follows:
- Bootstrap Sampling– This is how multiple subsets of the training data are created. In other words, we use a simple, naive process known as bootstrapping. We will take random samples from our original dataset, and these will be replaced. It means that in each subset, some data points may be repeats of the original list, and some other original data points may be left out.
- Train Models– For each subset, train an identical model (e.g., decision trees) independently on the corresponding subset of data. The bootstrap samples are trained independently and in parallel with each other using weak or base learners.
- Aggregate Predictions– After training the models, the predictions from each model are aggregated, typically by voting or averaging, to make the final prediction. For regression, this is the average of all the outputs that were predicted by the individual classifiers. This is referred to as soft voting. For classification problems, it is just the highest class label that is in the majority of votes. This is known as hard voting.
Types of Bagging Algorithms
There are various popular bagging algorithms, and the most common are as follows:
Random Forest is one of the most popular bagging techniques, that aggregates the forecasts from several decision trees trained on arbitrary feature and sample subsets. When it comes to reducing overfitting and raising prediction accuracy, random forests work incredibly well. To reduce the correlation between the final predictions made by each subtree, Random Forest modifies the technique used to train the sub-trees. Random forest algorithms are
Decision trees or bagged decision trees are not as good as Random Forests. They are greedy, and therefore the greediness of decision trees, such as CART, becomes a concern. They use an error-minimising greedy method to determine which variable to split on. Because of this, decision trees can exhibit strong structural similarities even when using Bagging, which leads to highly correlated predictions.
Pros and Cons of Bagging
There are numerous pros and cons of bagging that the bagging algorithm utilises when used for regression or classification problems in machine learning:
Pros of Bagging
- Reduced Variance– Bagging can lower the variance in a learning system. This is especially useful for high-dimensional data since missing values can increase variance, which increases the likelihood of overfitting and hinders appropriate generalisation to new data sets.
- Resistance to Overfitting– Compared to a single model, the ensemble is less likely to overfit as each model is trained on a separate subset.
- Easy implementation– Combining base learner or estimator predictions to enhance model performance is made simple by libraries like scikit-learn, etc in Python.
- Parallel Training- Bagging algorithms can be parallelised, which speeds up training because each model is trained individually.
Cons of Bagging
- Increased Computation– Bagging necessitates the training of several models, which raises memory and computational costs.
- Loss of interpretability– Because bagging involves averaging across forecasts, it is challenging to get extremely accurate business insights. Although the result is more accurate than any single data point, a single classification or regression model may also produce an output that is more accurate if the data set is more full or accurate.
- Not Reduced Bias– Although bagging reduces the variance, bias is left unaddressed. The performance of the base model won’t be appreciably enhanced by bagging if it is extremely biassed.
What is Boosting?
Boosting is an ensemble learning technique that turns a group of weak learners into a strong learner to reduce training errors. It is a process that goes step by step, with each new model trying to fix the mistakes made by the previous one. The preceding model is necessary for the subsequent models to function. Increasing algorithmic strength can help your data mining efforts become more predictive.
Boosting method involves teaching students in a stepwise manner. First, they fit basic models to the data and then check the data for flaws. The basic idea behind boosting is to build the model by fitting a simple model on the data to correct errors from previously built models by giving more weight to misclassified or difficult-to-predict data points. The primary objective of Boosting is to convert weak learners into strong learners by focusing on the errors made in each iteration.
Working Mechanism
Boosting in machine learning works as follows:
- Training Initial Model- Firstly, an initial model is trained on a complete dataset, and relevant predictions are made by the algorithms.
- Error Calculation- It saves the errors (misclassifications or incorrect predictions) from the first created model and identifies higher weights to those misclassified data points, making them more important in the next iteration.
- Training Next Model- A second model is trained on the same dataset but with more attention (higher weights) on the misclassified points. This process goes on, where each model hereafter is created by minimising the errors of the one preceding it.
- Aggregate Predictions- The predictions from all models are averaged in the final step. Unlike in Bagging, models are treated equally. Boosting will treat models very “unequally” by assigning a weight to each model based on how well it has performed.
Types of Boosting Algorithms
There are various popular boosting algorithms, and the most common are as follows:
- AdaBoost (Adaptive Boosting)
To reduce the training error, this approach iteratively finds misclassified data points and modifies their weights. It is one of the first boosting algorithms, each “weak” learner in AdaBoost focuses on the errors that have been made by the previous learner. The final prediction is a weighted sum of all “weak” learners.
The gradient boosting method repeatedly identifies misclassified data items and adjusts their weights to lower the training error. Model training in gradient boosting is sequential and is done by minimising a loss function using gradient descent. Every model aims to correct the residuals (errors) of the previous model.
- XGBoost, LightGBM, CatBoost
These are versions of Gradient Boosting that have been optimised to efficiently handle large datasets. They deliver improved performance via regularisation, faster training, and missing data when contrasted. With. XGBoost is a gradient-boosting system built for computational speed and scale, utilising many CPU cores.
Pros and Cons of Boosting
There are numerous pros and cons of boosting that the boosting algorithm utilises when used for regression or classification problems in machine learning:
Pros of Boosting
- Reduction in Bias and Variance– Boosting addresses both bias and variance by sequentially focusing on difficult-to-predict examples. It is commonly seen in shallow decision trees and logistic regression models.
- Ease of Implementation– Boosting can be used with several hyper-parameter tuning options to improve fitting. No data preprocessing is required, and boosting algorithms like have built-in routines to handle missing data.
- Adaptable to Different Loss Functions– Boosting can be applied to a wide range of loss functions, making it versatile across different problem types.
- Improved Accuracy– Boosting often leads to highly accurate models, especially for classification tasks.
- Computational Efficiency– Since boosting algorithms only select features that increase their predictive power during training, it can help to reduce dimensionality as well as increase computational efficiency.
Cons of Boosting
- Overfitting Risk- Since Boosting focuses on lowering errors iteratively, it can lead to overfitting, especially with noisy data.
- Sequential Training- Boosting models are trained sequentially, which means that they can not be parallelised without difficulty, making it slower to train than Bagging.
- Complexity- Boosting models can be complicated and harder to interpret compared to less complex models like decision trees.
- Intense computation- Sequential training in boosting is difficult to scale up. Since every estimator is built on its predecessors, boosting models can be computationally costly for organisations.
Similarities between Bagging and Boosting
Although bagging and boosting differ in machine learning models, there are some similarities between them:
- Both Bagging and Boosting are ensemble methods, because of this they integrate more than one model to create a stronger standard model. The idea at the back of ensemble mastering is that a group of models operating together can outperform individual models.
- Both generate several training data sets utilising random sampling.
- Both strategies rely on training several models (typically known as base models) to enhance predictions.
- Both make the final decision using the average of the N learners or a group of learners, also known as Majority Voting.
- Both Bagging and Boosting aim to reduce the likelihood of overfitting. Bagging does this with the aid of decreasing the variance, at the same time as Boosting reduces both bias and variance, probably addressing overfitting in complicated models.
- In both techniques, the very last prediction is a combination (aggregation) of the predictions of the individual model. In Bagging, the predictions are aggregated by vote casting or averaging, at the same time as in Boosting, the models are combined in a weighted way.
- Both are good at reducing variance and offer higher stability.
- Both Bagging and Boosting may be carried out for classification and regression tasks.
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Differences between Bagging and Boosting
The key differences between Bagging and Boosting are as follows:
| Basis |
Bagging |
Boosting |
Definition |
Bagging is a simplest way of combining predictions that
belong to the same type in machine learning.
Boosting is a way of combining predictions that
belong to the different types of machine learning.
| Objective |
The objective of bagging is to reduce the variance and not bias. |
The objective of boosting is to reduce the variance and bias both. |
| Training Method |
Bagging allows training the models independently on multiple data sets. |
Bagging allows us to train the models sequentially by correcting the errors in the previous ones. |
| Model building |
Models are built independently in bagging. |
Models are influenced and built by the performance of the earlier models. |
| Error Focus |
There is no focus on errors in any specific data points. |
There is a focus on misclassified data points in each iteration. |
| Overfitting issues |
There is a lesser risk of overfitting issues. |
There is a higher risk of overfitting issues. |
| Parallelisation |
Bagging can be parallelised. |
Boosting has difficulty in parallelisation as there is a sequential training. |
| Computation |
Bagging requires low computation compared to boosting. |
Boosting requires very high computation compared to bagging because of sequential. |
Handling of Data Subsets |
Here, each model is trained on random bootstrapped (resampled) subsets of the data.
The same data are used for training, but weights are modified based on previous errors. |
| Base Learners |
Usually use advanced models as foundation learners, such as deep decision trees. |
It uses the basic models as weak learners, such as shallow decision trees. |
| Effectiveness of Noisy data |
In general, noisy data is better handled because it decreases variation. |
More prone to noise since boosting frequently concentrates on unpredictable cases, which could involve noise |
| Model Dependency |
Models can train concurrently and are not dependent on one another. |
Models are interdependent; every new model fixes the flaws in the preceding one. |
| Performance on Large Datasets |
Larger datasets are better suited for it because it can be parallelised for efficiency. |
It is slower on larger datasets since they are sequential and require more processing power. |
| Training Data Distribution |
Each model here gets some random subsets. |
Here, more weightage is given to misclassified points. |
Real-World Applications of Bagging and Boosting
The real-world applications of the various ensemble methods i.e., bagging and boosting are as follows:
Bagging Applications
- Security- Security is a major concern in the IT sector, and to protect it from attacks like denial of service (DDoS), intrusion codes, and malware, the bagging ensemble method is the best choice.
- Healthcare– Employing large, complicated datasets to predict medical problems and diseases, when individual decision trees may overfit.
- Finance– Bagging techniques aid in the reduction of overfitting and enhance the precision of fraudulent transaction prediction in fraud detection. To reduce financial losses, for instance, boosting techniques in financial product pricing research and credit card fraud detection increase the accuracy of evaluating large data sets.
- Emotion Recognition- Even though the majority of the major players in the industry have stated that deep learning forms the basis of speech recognition technology, ensemble learning can also produce such acceptable results in the field of emotion recognition.
Boosting Applications
- Finance- Boosting is frequently employed in a variety of financial activities, such as credit scoring systems and fraud detection, where a tiny fraction of incorrectly categorised data can result in considerable errors.
- Healthcare- Boosting is used to reduce mistakes in the predictions made by medical data, such as the survival rates of cancer patients and cardiovascular risk factors.
- Recommendation Systems– By fixing mistakes from previous iterations and increasing the relevance of recommendations, boosting helps in the fine-tuning of recommendation systems.
- Information Technology- In the IT field also boosting algorithms are used for efficiencies, such as search engines use gradient-boosted regression trees for page rankings and many more.
Also Read: Uses of Artificial Intelligence
Conclusion
Bagging and Boosting are effective ensemble methods, however, each of them has a different approach as well as a different goal. Bagging is used to reduce the variance of the predictions while boosting is more reliant on increasing the prediction accuracy by reducing the bias and variance of the learners. Knowing how, and when to apply those techniques may help improve the overall quality of machine-learning models across different domains.
However, the selection of one technique over the other depends on the characteristics of the dataset, the architecture of the model, and the amount of computational resources required to attain the desired level of accuracy in the predictions. Both methods provide great opportunities for improving machine learning models.
FAQs
In bagging, distinct random subsets of the data are used to train the models concurrently and independently. In contrast, by sequentially boosting train models, each one learns from the mistakes of the previous one. Furthermore, boosting allocates weights according to accuracy, whereas bagging usually only entails the simple average of models.
Bagging is a technique ensemble method which is used to enhance the performance of a model, by training it on different portions or divisions of the training dataset and combining the outputs of all the trained models. For instance, in a classification based on decision trees, many decision trees are modelled over different random samples, and the various outcomes are integrated in some way such as taking the average or final output.
It is termed a bagging method since it develops many decision trees through the use of bootstrapped sampling techniques whereby subsets of data sampled from the main dataset is used to generate many trees. There are separate trees created, and their predictions are averaged (in regression) or voted upon (in classification) so that a net more precise and consistent prediction can be made.
When classifying, the decision tree-based learners would be used in Random Forest which is one example of bagging, and Pasting is also another method of bagging which is similar to bagging yet there is no replacement during the sampling. There’s also bagging with different models e.g., bagging with neural networks or K nearest neighbours (KNN) instead of decision trees as base learners.
Bagging is exemplified in the Random Forest and Pasting technique. For boosting, the most used are AdaBoost and Gradient Boosting. Random Forest aims to increase precision by controlling the variance while AdaBoost seeks to fix the misclassified data points.
