In bagging, a random sample of data in a training set is selected with replacement—meaning that the individual data points can be chosen more than once. After generating several data samples, these weak models are then trained independently. Depending on the type of task—regression or classification, for example—the average or majority of those predictions yield a more accurate estimate.

As a note, the random forest algorithm is considered an extension of the bagging method, using both bagging and feature randomness to create an uncorrelated forest of decision trees.

Ensemble learning gives credence to the idea of the “wisdom of crowds,” which suggests that the decision-making of a larger group of people is typically better than that of an individual expert. Similarly, ensemble learning refers to a group (or ensemble) of base learners, or models, which work collectively to achieve a better final prediction.

A single model, also known as a base or weak learner, may not perform well individually due to high variance or high bias. However, when weak learners are aggregated, they can form a strong learner, as their combination reduces bias or variance, yielding better model performance.

Ensemble methods frequently use decision trees for illustration. This algorithm can be prone to overfitting, showing high variance and low bias, when it hasn’t been pruned. Conversely, it can also lend itself to underfitting, with low variance and high bias, when it’s very small, like a decision stump, which is a decision tree with one level.

Remember, when an algorithm overfits or underfits to its training set, it cannot generalize well to new data sets, so ensemble methods are used to counteract this behavior to allow for generalization of the model to new data sets. While decision trees can exhibit high variance or high bias, it’s worth noting that it is not the only modeling technique that leverages ensemble learning to find the “sweet spot” within the bias-variance tradeoff.

Bagging and boosting are two main types of ensemble learning methods. As highlighted in this study (link resides outside ibm.com), the main difference between these learning methods is how they are trained.

In bagging, weak learners are trained in parallel, but in boosting, they learn sequentially. This means that a series of models is constructed and with each new model iteration, the weights of the misclassified data in the previous model are increased.

This redistribution of weights helps the algorithm identify the parameters that it needs to focus on to improve its performance. AdaBoost, which stands for “adaptative boosting algorithm,” is one of the most popular boosting algorithms as it was one of the first of its kind. Other types of boosting algorithms include XGBoost, GradientBoost and BrownBoost.

Another difference in which bagging and boosting differ are the scenarios in which they are used. For example, bagging methods are typically used on weak learners that exhibit high variance and low bias, whereas boosting methods are used when low variance and high bias are observed.

In 1996, Leo Breiman (link resides outside ibm.com) introduced the bagging algorithm, which has three basic steps:

1. Bootstrapping: Bagging leverages a bootstrapping sampling technique to create diverse samples. This resampling method generates different subsets of the training data set. It does so by selecting data points at random and with replacement. This means that each time you select a data point from the training data set, you are able to select the same instance multiple times. As a result, a value or instance repeated twice (or more) in a sample.
2. Parallel training: These bootstrap samples are then trained independently and in parallel with each other using weak or base learners.
3. Aggregation: Finally, depending on the task (that is, regression or classification), an average or a majority of the predictions are taken to compute a more accurate estimate. In the case of regression, an average is taken of all the outputs predicted by the individual classifiers; this is known as soft voting. For classification problems, the class with the highest majority of votes is accepted; this is known as hard voting or majority voting.

There are several key advantages and challenges that the bagging method presents when used for classification or regression problems. The key benefits of bagging include:

• Ease of implementation: Python libraries such as scikit-learn (also known as sklearn) make it easy to combine the predictions of base learners or estimators to improve model performance. Their documentation (link resides outside ibm.com) lays out the available modules that you can use in your model optimization.
• Reduction of variance: Bagging can reduce the variance within a learning algorithm. This is particularly helpful with high-dimensional data, where missing values can lead to higher variance, making it more prone to overfitting and preventing accurate generalization to new data sets.

The key challenges of bagging include:

• Loss of interpretability: It’s difficult to draw very precise business insights through bagging because due to the averaging involved across predictions. While the output is more precise than any individual data point, a more accurate or complete data set could also yield more precision within a single classification or regression model.
• Computationally expensive: Bagging slows down and grows more intensive as the number of iterations increase. Thus, it’s not well suited for real-time applications. Clustered systems or a large number of processing cores are ideal for quickly creating bagged ensembles on large test sets.
• Less flexible: As a technique, bagging works particularly well with algorithms that are less stable. One that are more stable or subject to high amounts of bias do not provide as much benefit as there’s less variation within the data set of the model. As noted in the Hands-On Guide to Machine Learning (link resides outside ibm.com), “bagging a linear regression model will effectively just return the original predictions for large enough b.”

The bagging technique is used across many industries, providing insights for both real-world value and interesting perspectives, such as in the GRAMMY Debates with Watson. Key use cases include:

• Healthcare: Bagging has been used to form medical data predictions. For example, research (link resides outside ibm.com) shows that ensemble methods have been used for an array of bioinformatics problems, such as gene and/or protein selection to identify a specific trait of interest. More specifically, this research (link resides outside ibm.com) delves into its use to predict the onset of diabetes based on various risk predictors.
• IT: Bagging can also improve the precision and accuracy in IT systems, such as ones network intrusion detection systems. Meanwhile, this research (link resides outside ibm.com) looks at how bagging can improve the accuracy of network intrusion detection—and reduce the rates of false positives.
• Environment: Ensemble methods, such as bagging, have been applied within the field of remote sensing. More specifically, this research (link resides outside ibm.com) shows how it has been used to map the types of wetlands within a coastal landscape.
• Finance: Bagging has also been used with deep learning models in the finance industry, automating critical tasks, including fraud detection, credit risk evaluations and option pricing problems. This research (link resides outside ibm.com) demonstrates how bagging among other machine learning techniques have been leveraged to assess loan default risk. This study (link resides outside ibm.com) highlights how bagging helps to minimize risk by preventing credit card fraud within banking and financial institutions.