XGBoost - The Choice Of Most Champions

XGBoost

You see the X in XGBoost, right? That literally stands for Extreme Gradient Boosting; basically, Gradient Boosting but all swirled up in steroids; thus, calling Extreme Gradient Boosting for a reason. XGBoost helps reduce error significantly while at the same time require a minimum amount of computing resources in the shortest duration of time with this beautiful combination approach for both software and hardware optimization techniques.

Ensemble Learning

When we talk about XGBoost, we concur it is an approach to ensemble learning. But what exactly is Ensemble Learning and why do we use it?

Ensemble learning can be understood as this process where generation and combination of different numerous models, let’s say classifiers and experts are performed to solve a specific computational intelligence problem. It helps to produce better predictive performance with minimal error on regression problems and higher accuracy in the case of classification. We have assumed for a really long time, that the choice of the right algorithms is the most important task alone. But in ensemble learning, it has proved that gathering models into ensembles can produce an even better outcome.

Ensemble Methods in Machine Learning

This approach of ensemble learning, where we combine several base models such that one optimal predictive model can be obtained for Machine Learning is the ensemble method. Using the approach of ensemble methods such as bagging and random forest, we can obtain outcomes at a different level of the decision trees, and instead of building one single model hoping it will provide the best prediction, the different levels of the trees can be taken into account for different models and average them to obtain one final model for prediction. Decision Trees however aren’t the only method to approach ensemble learning but one of the used one. Moreover, this method of ensemble has been evolving in the field of Machine Learning to produce better predictions with the different use cases of numerous machine learning techniques for various models to produce the optimal predictive model.

Why is XGBoost so good?

The reason for this vague outstanding performance of XGBoost can be attributed to various reasons. However, it can be classified basically into two parts, one for the optimization of the system or hardware and another for the enhancements at the algorithm level.

The XGBoost penalizes for overfitting with regularization methods of L1 and L2 i.e., Lasso Regression and Ridge Regression. It also can handle the missing value which is one of the major drawbacks in most of the other algorithms. This sparsity awareness helps it outperform other algorithms too. Besides, the deployment of distributed weighted Quantile Sketch algorithm helps in efficient proposal calculation for optimal split points in datasets. Also, the requirement of specifically declaring the number of boosting iterations isn’t there as the cross-validation provided built-in into the algorithm takes care of it. These numerous approaches in the XG Boost help it stand out algorithmically.

Besides, it isn’t the excelsior algorithmic level alone that makes XGBoost so lucrative to use. Its requirement of minimal hardware usage to run this algorithm can be attributed to the parallel processing implementation thus significantly improving the run time in comparison to any of the other algorithms. Moreover, the depth-first approach implementation in XGBoost through the max_depth parameter provides the reverse tree pruning which is comparatively highly effective to improve computational performance. Moreover, the cache awareness in XGBoost and enhancements such as out-of-core computing helps to handle data-frames that are bigger than size to fit into memory itself. This terrific hardware optimization approach fit into the algorithm itself makes XGBoost too hard to resist to solve Machine Learning problems.

Looking Forward

Machine Learning isn’t only tied to choose the best algorithms. A lot goes into play to obtain the perfect solution from the right configuration through hyper-parameters tuning as per the dataset, computational complexities, and how easily can these algorithms be implemented. There is so much iteration outside the machine learning algorithm itself – this is more about the creativity of use than just about hard numbers and a tedious mundane number crunching and output. A traditional approach can be beaten by small tweaks on the parameters alone at times and that’s the beauty of Machine Learning. XGBoost is here to stay for long, as the choice for the best of champions in Machine Learning Competitions making it the undisputed King of Machine Learning Algorithm as of today. Looking forward, to this ever-evolving field – the state of the art research is ongoing day in and day out at the biggest of technology giants' research facilities. LightGBM got released by Microsoft Research in 2016 which one another gradient-boosting tree framework become an attraction to data scientists, the Russian Tech Giant Yandex Technology released Catboost which shows promising results. In due time, there are numerous other stronger challengers to come to take away this title from XGBoost. Until then, XGBoost is here to stay as the favorite for the majority of champions.

Conclusion

In this article, we discussed in-depth about XGBoost and its widely accepted feats and the reason why it has been able to outperform the majority of machine learning algorithms out there. We understood the foundation of the XGBoost itself with a demonstrative analogy with the real-world scenario for a deeper understanding of what makes this algorithm so unique. Moreover, we also discussed Ensemble Learning and Ensemble Methods in Machine Learning. At last, we learned about the major attributes contributing to the ability of XGBoost to act this superb and the future prospects and other development in Machine Learning Algorithms.