What is XGBoost?
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting
framework. In prediction problems involving unstructured data (images,
text, etc.) artificial neural networks tend to outperform all other
algorithms or frameworks. However, when it comes to small-to-medium
structured/tabular data, decision tree based algorithms are considered
best-in-class right now. Please see the chart below for the evolution of
tree-based algorithms over the years.
XGBoost algorithm was developed as a research project at the University of Washington. Tianqi Chen and Carlos Guestrin
presented their paper at SIGKDD Conference in 2016 and caught the
Machine Learning world by fire. Since its introduction, this algorithm
has not only been credited with winning numerous Kaggle competitions but
also for being the driving force under the hood for several
cutting-edge industry applications. As a result, there is a strong
community of data scientists contributing to the XGBoost open source
projects with ~350 contributors and ~3,600 commits on GitHub. The algorithm differentiates itself in the following ways:
- A wide range of applications: Can be used to solve regression, classification, ranking, and user-defined prediction problems.
- Portability: Runs smoothly on Windows, Linux, and OS X.
- Languages: Supports all major programming languages including C++, Python, R, Java, Scala, and Julia.
- Cloud Integration: Supports AWS, Azure, and Yarn clusters and works well with Flink, Spark, and other ecosystems.
How to build an intuition for XGBoost?
Decision
trees, in their simplest form, are easy-to-visualize and fairly
interpretable algorithms but building intuition for the next-generation
of tree-based algorithms can be a bit tricky. See below for a simple
analogy to better understand the evolution of tree-based algorithms.
Imagine
that you are a hiring manager interviewing several candidates with
excellent qualifications. Each step of the evolution of tree-based
algorithms can be viewed as a version of the interview process.
- Decision Tree: Every hiring manager has a set of criteria such as education level, number of years of experience, interview performance. A decision tree is analogous to a hiring manager interviewing candidates based on his or her own criteria.
- Bagging: Now imagine instead of a single interviewer, now there is an interview panel where each interviewer has a vote. Bagging or bootstrap aggregating involves combining inputs from all interviewers for the final decision through a democratic voting process.
- Random Forest: It is a bagging-based algorithm with a key difference wherein only a subset of features is selected at random. In other words, every interviewer will only test the interviewee on certain randomly selected qualifications (e.g. a technical interview for testing programming skills and a behavioral interview for evaluating non-technical skills).
- Boosting: This is an alternative approach where each interviewer alters the evaluation criteria based on feedback from the previous interviewer. This ‘boosts’ the efficiency of the interview process by deploying a more dynamic evaluation process.
- Gradient Boosting: A special case of boosting where errors are minimized by gradient descent algorithm e.g. the strategy consulting firms leverage by using case interviews to weed out less qualified candidates.
- XGBoost: Think of XGBoost as gradient boosting on ‘steroids’ (well it is called ‘Extreme Gradient Boosting’ for a reason!). It is a perfect combination of software and hardware optimization techniques to yield superior results using less computing resources in the shortest amount of time.
Why does XGBoost perform so well?
XGBoost
and Gradient Boosting Machines (GBMs) are both ensemble tree methods
that apply the principle of boosting weak learners (CARTs
generally) using the gradient descent architecture. However, XGBoost
improves upon the base GBM framework through systems optimization and
algorithmic enhancements.
System Optimization:
- Parallelization: XGBoost approaches the process of sequential tree building using parallelized implementation. This is possible due to the interchangeable nature of loops used for building base learners; the outer loop that enumerates the leaf nodes of a tree, and the second inner loop that calculates the features. This nesting of loops limits parallelization because without completing the inner loop (more computationally demanding of the two), the outer loop cannot be started. Therefore, to improve run time, the order of loops is interchanged using initialization through a global scan of all instances and sorting using parallel threads. This switch improves algorithmic performance by offsetting any parallelization overheads in computation.
- Tree Pruning: The stopping criterion for tree splitting within GBM framework is greedy in nature and depends on the negative loss criterion at the point of split. XGBoost uses ‘max_depth’ parameter as specified instead of criterion first, and starts pruning trees backward. This ‘depth-first’ approach improves computational performance significantly.
- Hardware Optimization: This algorithm has been designed to make efficient use of hardware resources. This is accomplished by cache awareness by allocating internal buffers in each thread to store gradient statistics. Further enhancements such as ‘out-of-core’ computing optimize available disk space while handling big data-frames that do not fit into memory.
Algorithmic Enhancements:
- Regularization: It penalizes more complex models through both LASSO (L1) and Ridge (L2) regularization to prevent overfitting.
- Sparsity Awareness: XGBoost naturally admits sparse features for inputs by automatically ‘learning’ best missing value depending on training loss and handles different types of sparsity patterns in the data more efficiently.
- Weighted Quantile Sketch: XGBoost employs the distributed weighted Quantile Sketch algorithm to effectively find the optimal split points among weighted datasets.
- Cross-validation: The algorithm comes with built-in cross-validation method at each iteration, taking away the need to explicitly program this search and to specify the exact number of boosting iterations required in a single run.
Where is the proof?
We used Scikit-learn’s ‘Make_Classification’
data package to create a random sample of 1 million data points with 20
features (2 informative and 2 redundant). We tested several algorithms
such as Logistic Regression, Random Forest, standard Gradient Boosting,
and XGBoost.
As
demonstrated in the chart above, XGBoost model has the best combination
of prediction performance and processing time compared to other
algorithms. Other rigorous benchmarking studies have produced similar results. No wonder XGBoost is widely used in recent Data Science competitions.
“When in doubt, use XGBoost” — Owen Zhang, Winner of Avito Context Ad Click Prediction competition on Kaggle
So should we use just XGBoost all the time?
When
it comes to Machine Learning (or even life for that matter), there is
no free lunch. As Data Scientists, we must test all possible algorithms
for data at hand to identify the champion algorithm. Besides, picking
the right algorithm is not enough. We must also choose the right
configuration of the algorithm for a dataset by tuning the hyper-parameters.
Furthermore, there are several other considerations for choosing the
winning algorithm such as computational complexity, explainability, and
ease of implementation. This is exactly the point where Machine Learning
starts drifting away from science towards art, but honestly, that’s
where the magic happens!
What does the future hold?
Machine
Learning is a very active research area and already there are several
viable alternatives to XGBoost. Microsoft Research recently released LightGBM framework for gradient boosting that shows great potential. CatBoost
developed by Yandex Technology has been delivering impressive
bench-marking results. It is a matter of time when we have a better
model framework that beats XGBoost in terms of prediction performance,
flexibility, explanability, and pragmatism. However, until a time when a
strong challenger comes along, XGBoost will continue to reign over the
Machine Learning world!
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