Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. Boosting algorithms can improve the predictive power of your data mining initiatives.
In boosting, a random sample of data is selected, fitted with a model and then trained sequentially—that is, each model tries to compensate for the weaknesses of its predecessor. With each iteration, the weak rules from each individual classifier are combined to form one, strong prediction rule.
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 are frequently illustrated using decision trees as this algorithm can be prone to overfitting (high variance and low bias) when it hasn’t been pruned and it can also lend itself to underfitting (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 dataset, it cannot generalize well to new datasets, so ensemble methods are used to counteract this behavior to allow for generalization of the model to new datasets. 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 the way in which they are trained. In bagging, weak learners are trained in parallel, but in boosting, they learn sequentially. This means that a series of models are 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 between bagging and boosting is in how they are used. For example, bagging methods are typically used on weak learners that exhibit high variance and low bias, whereas boosting methods are leveraged when low variance and high bias is observed. While bagging can be used to avoid overfitting, boosting methods can be more prone to this (link resides outside ibm.com) although it really depends on the dataset. However, parameter tuning can help avoid the issue.
As a result, bagging and boosting have different real-world applications as well. Bagging has been leveraged for loan approval processes and statistical genomics while boosting has been used more within image recognition apps and search engines.
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Boosting methods are focused on iteratively combining weak learners to build a strong learner that can predict more accurate outcomes. As a reminder, a weak learner classifies data slightly better than random guessing. This approach can provide robust results for prediction problems, and can even outperform neural networks and support vector machines for tasks like image retrieval (link resides outside ibm.com).
Boosting algorithms can differ in how they create and aggregate weak learners during the sequential process. Three popular types of boosting methods include:
- Adaptive boosting or AdaBoost: Yoav Freund and Robert Schapire are credited with the creation of the AdaBoost algorithm. This method operates iteratively, identifying misclassified data points and adjusting their weights to minimize the training error. The model continues optimize in a sequential fashion until it yields the strongest predictor.
- Gradient boosting: Building on the work of Leo Breiman, Jerome H. Friedman developed gradient boosting, which works by sequentially adding predictors to an ensemble with each one correcting for the errors of its predecessor. However, instead of changing weights of data points like AdaBoost, the gradient boosting trains on the residual errors of the previous predictor. The name, gradient boosting, is used since it combines the gradient descent algorithm and boosting method.
- Extreme gradient boosting or XGBoost: XGBoost is an implementation of gradient boosting that’s designed for computational speed and scale. XGBoost leverages multiple cores on the CPU, allowing for learning to occur in parallel during training.
Benefits and challenges of boosting
There are a number of key advantages and challenges that the boosting method presents when used for classification or regression problems.
The key benefits of boosting include:
- 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. In Python, the scikit-learn library of ensemble methods (also known as sklearn.ensemble) makes it easy to implement the popular boosting methods, including AdaBoost, XGBoost, etc.
- Reduction of bias: Boosting algorithms combine multiple weak learners in a sequential method, iteratively improving upon observations. This approach can help to reduce high bias, commonly seen in shallow decision trees and logistic regression models.
- Computational Efficiency: Since boosting algorithms only select features that increase its predictive power during training, it can help to reduce dimensionality as well as increase computational efficiency.
The key challenges of boosting include:
- Overfitting: There’s some dispute in the research (link resides outside ibm.com) around whether or not boosting can help reduce overfitting or exacerbate it. We include it under challenges because in the instances that it does occur, predictions cannot be generalized to new datasets.
- Intense computation: Sequential training in boosting is hard to scale up. Since each estimator is built on its predecessors, boosting models can be computationally expensive, although XGBoost seeks to address scalability issues seen in other types of boosting methods. Boosting algorithms can be slower to train when compared to bagging as a large number of parameters can also influence the behavior of the model.
Boosting algorithms are well suited for artificial intelligence projects across a broad range of industries, including:
Healthcare: Boosting is used to lower errors in medical data predictions, such as predicting cardiovascular risk factors and cancer patient survival rates. For example, research (link resides outside ibm.com) shows that ensemble methods significantly improve the accuracy in identifying patients who could benefit from preventive treatment of cardiovascular disease, while avoiding unnecessary treatment of others. Likewise, another study (link resides outside ibm.com) found that applying boosting to multiple genomics platforms can improve the prediction of cancer survival time.
IT: Gradient boosted regression trees are used in search engines for page rankings, while the Viola-Jones boosting algorithm is used for image retrieval. As noted by Cornell (link resides outside ibm.com), boosted classifiers allow for the computations to be stopped sooner when it’s clear in which way a prediction is headed. This means that a search engine can stop the evaluation of lower ranked pages, while image scanners will only consider images that actually contains the desired object.
Finance: Boosting is used with deep learning models to automate critical tasks, including fraud detection, pricing analysis, and more. For example, boosting methods in credit card fraud detection and financial products pricing analysis (link resides outside ibm.com) improve the accuracy of analyzing massive data sets to minimize financial losses.
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This paper shows how quantum techniques can improve the time complexity of classical AdaBoost.
IBM Research studies a Heterogeneous Newton Boosting Machine (HNBM) in which the base hypothesis class may vary across boosting iterations.