XGBoost- What is it?
XGBoost is an ensemble decision tree-based machine learning algorithm and utilizes a gradient boosting framework. Artificial neural networks mostly outperform other frameworks or algorithms when predicting problems with the text, images, and other unstructured data. Things are vastly different when it comes to medium or small tabular/structured data. Why? Because algorithms based on decision trees are arguably the best solution.
The XGBoost algorithm was the result of a research project organized at the University of Washington. Carlos Guestrin and Tianqi Chen introduced their paper at the 2016s SIGKDD conference. Since then, XGBoost became a renowned algorithm that revolutionized the machine learning landscape.
Since being introduced, this algorithm received tons of credits, including several Kaggle competitions. It also received recognition for influence for various state-of-the-art industry applications. Consequentially, a massive group of data scientists is contributing to different XGBoost related open source projects. GitHub has over three hundred contributors, and thirty-six hundred commits so far. Learn how this algorithm stands out from others.
How XGBoost Stands Out
A Vast Range of Applications
XGBoost lets you use a wide range of applications for solving user-defined prediction, ranking, classification, and regression problems.
It runs smoothly on OSX, Linux, and Windows.
Variety of Languages
XGBoost supports most programming languages including, Julia, Scala, Java, R, Python, C++.
Integration with Cloud
The XGBoost machine learning algorithm supports Yarn clusters, azure, and AWS. It also works incredibly well with other Spark, Flink, and various ecosystems.
Building an XG Boost Intuition
One of the reasons why decision trees are so popular is that visualizing them is quite straightforward in their most basic form. Their algorithms are also easy to interpret, but creating intuition for the upcoming generation of algorithms (tree-based) can be a bit complicated. Here is an analogy to comprehend the tree-based algorithms and their evolution.
Think of yourself as a hiring manager, talking to several highly qualified candidates. Every step of the tree-based algorithm’s evolution is like a part of the interview process.
Each hiring manager follows a set of criteria, including experience, education, and interview performance. It would be fair to compare a decision tree to a hiring manager questioning candidates based on her or his criteria.
Multiple people are in charge of the interviews now, and everyone in the panel has a vote. Bootstrap aggregating or bagging involves amalgamating inputs from each interviewer for the conclusive decision with the help of an unbiased voting process.
The Random Forest is a bagging algorithm that randomly selects a sub-component of features. To put it another way, each interviewer will test the candidate based on randomly chosen qualifications (for example, technical interviews to test programming skills.)
Boosting is an alternative procedure where every interviewer changes the evaluation criteria depending on the feedback received from previous interviewers. It makes for a highly efficient interview process as it helps deploy more powerful evaluation procedures.
The gradient boosting process minimizes errors through gradient descent algorithm. For example, strategy consulting firms use case interviews to eliminate candidates who are not as qualified as other applicants.
XGBoosting is a significantly more efficient version of gradient boosting, hence its name: Extreme Gradient Boosting.It is an ideal combination of hardware and software optimization techniques to achieve superior results by utilizing minimal computing resources in short periods.
What is the Secret behind XGBoost’s Excellent Performance?
Gradient boosting machines and XGBoost are ensemble tree methods. They use the gradient descent architecture to apply the principle to boost poor learners (CARTs.) Extreme Gradient Boostsignificantly improves the GBM framework by implementing algorithmic enhancements and systems optimization.
XGBoost uses parallelized implementation to boot for the sequential tree building process. It is possible because of the interchangeable characteristic of loops utilized for creating base learners. The inner loop counts the features while the outer loop itemizes a tree’s leaf nodes. Loop nesting restricts parallelization because it is impossible to start the outer loop without finishing the inner loop.
So, to achieve a better run time, the loops’ order interchanges with the help of initialization with a global scan of sorting and instances utilizing parallel threads. The switch will significantly improve algorithmic performance through offsetting parallelization overheads.
XGBoost utilizes ‘max_depth’ and ignores criterion first to prune trees backward. The approach is excellent for increasing computational performance.
The hardware optimization algorithm uses hardware resources efficiently. It is possible by allocating internal buffers in every thread for storing gradient stats. Other improvements like “out of core” computing use disk space and handle data frames that cannot fit inside the memory.
It helps penalize complex models with the help of Ridge and LASSO to avoid overfitting.Cross-Validation
The XGBoost machine learning algorithm has a cross-validation method implemented in each iteration. It eliminates the need for elaborate programming this search and specifying the precise amount of boosting iterations needed per run.
XGBoost adds sparse features by automatically understanding the essential value and handles various sparsity patterns with extraordinary efficiency.
Weighted Quantile Sketch
Extreme Gradient boost implements the distributed sketch algorithm to find the ideal split pints in weighted datasets.
XGBoost Could be the Future of Machine Learning
Data scientists must test every algorithm to identify the best option. Besides, choosing the correct algorithm, while excellent, is not enough. Selecting the right configuration for a particular dataset is also crucial. What’s more, you have to consider several elements to choose the best algorithm. Pay attention to things like implementation, explainability, and computational complexity to determine the algorithm that would suit you the most.
Finally, machine learning is a vast feed, and you will find tons of alternatives to Extreme Gradient Boost. Cat Boost and Light GBM are some recent alternatives delivering impressive results. That said, XGBoost reigns supreme in terms of explainability, pragmatism, flexibility, performance, and plenty of other things. Expect this machine-learning algorithm to stay on top until a worthy challenger comes along and shakes things up.