Boosting algorithms are leading the machine learning community in terms of popularity. And when we talk about the XGBoost, it is the most used algorithm to solve and win the Kaggle competitions. In 2015, Kaggle announced 29 winning solutions, and out of these, 17 used XGBoost. From this, we can sense the popularity of this algorithm, and hence it becomes essential to know about it. In this article, we will learn about XG-Boost in greater detail.
After going through this blog, we will be able to answer the following questions:
So let's start our discussion with our first topic, which is:
Artificial neural networks and deep learning lead the market for unstructured data like images, audio, and texts. At the same time, when we talk about small or medium-level structured data, tree-based algorithms dominate the market. And when we say tree, it all starts with the basic building block, i.e., Decision Trees. DTs were able to solve both classification and regression problems but suffered from the overfitting issues quickly. To tackle this, we ensembled multiple DTs with slight modifications in data formation. It created Bagging and Random Forest algorithms.
After that, researchers thought that ensembling trees randomly was time-consuming and computationally inefficient. Why not build trees sequentially and improve over those parts where previous trees failed. That's where boosting came into the picture. Later these boosting algorithms started utilizing the gradient descent algorithm to form trees sequentially and minimize the error in predictions; hence these algorithms are called Gradient Boosting. Later, researchers proposed model, algorithmic, and hardware optimizations to further improve the Gradient boosting algorithms' performance. The combination of all these optimizations over Gradient boosting is known as XG-Bo ost, and we will be discussing them in this article.
XGBoost, also known as Extreme Gradient Boosting, is a supervised learning technique that uses an ensemble approach based on the Gradient boosting algorithm. It is a scalable end-to-end tree boosting system, widely used by data scientists to achieve state-of-the-art results on many machine learning challenges. It can solve both classification and regression problems and achieve better results with minimal effort.
The initial version of this algorithm was implemented using the Gradient Boosting machines. Later after making this work an open-source, a large community of data scientists started contributing to the XGBoost projects and improved this algorithm further. With the help of such a great community, XGBoost has become a software library and can directly be installed into our systems. It supports various interfaces, including Python, R, C++, Julia, and Java. So let's first install this library and then learn what features it does provide.
There is official documentation as an installation guide for XGBoost on the XGBoost installation guide. For XGboost in python, Python Package Introduction (PyPI) is the best to start. We can install XGBoost using pip as:
sudo pip3 install xgboost
We know that installing an additional package is always an overhead thing and decreases popularity until it provides something great. It's the same as getting the payment option on WhatsApp; it will be more convenient than using an additional Google Pay application until google pay provides some advanced features. Still, XG-Boost manages to give us something exciting, and people do not hesitate to install a different package. So let's see what features it does provide.
There are multiple supports that XG_Boost provides us. These supports are growing over time as this framework is open-source, and people contribute continuously to enhance the features. Some of the most prominent features are:
These features and supports are growing continuously as the XG-Boost library is open source, and a large group of developers is contributing here. Looking into the details of this algorithm, we might be wondering, why other algorithms then? So let's understand this.
Indeed, XG-Boost's support is exciting and widely configurable, yet we can not say that it will always work best. There is nothing like one algorithm for all kinds of solutions in Machine Learning. It is always advisable to try out different algorithms and then decide which works best per our requirements. Sometimes accuracy is not the sole requirement that we expect from our machine learning model. We also want a decent amount of explainability, lesser computational complexity, and ease in deployment. These factors also help in selecting the best model for our requirements.
Before ending our discussion, let's understand one important thing, i.e., what are the hyperparameters involved in this algorithm that can be tuned to extract the best out of it.
To learn any machine learning algorithm, we must know the different factors that can affect the performance of our model when we try to fit that algorithm into our dataset. In XG-Boost, these factors are:
In addition to these, XG-Boost provides the functionality where we can define the grid of values (multiple values of these parameters in arrays), and the in-built cross-validation technique will find the best set of parameters.
XG-Boost provides great support and is very popular in the industry. So if there are some projects in our resume that uses this algorithm, then some most probable questions that can be asked o this topic are:
Here, we learned about the most famous algorithm to solve machine learning challenges over different competitive platforms, i.e., XG-Boost. We list down all the supports it provides to improve the performance over the standard tree algorithms. We also discussed the different hyperparameters to tune the performance of XG-Boost further. We hope you enjoyed the article.
Subscribe to get weekly content on data structure and algorithms, machine learning, system design and oops.