The world of gradient boosting has multiple different libraries for you to choose from. XGBoost is usually the one that comes to mind for most people, but you also have the option of going with LightGBM. Both of these gradient boosting frameworks can work really well, but you should also understand that one of them has advantages over the other. The XGBoost algorithm is a machine learning algorithm to improve how a decision tree works by applying gradient boosting to it. On the outside, it seems quite complicated, but it is simple when you get down to it.
A decision tree is simply a graph that works through various paths based on previous paths. You can think of it as an if-then decision tree. The machine learning algorithm can be used to make your models better. The main thing to note is that both libraries offer the functionality needed, and you can get extremely good results regardless. Working with the XGBoost machine learning library is also going to open you up to plenty of other interfaces that can provide you with the functionality you need as well.
What Is XGBoost?
As mentioned above, XGBoost is an exceptional open source library that applies a gradient boosting algorithm to a decision tree to make it more accurate. It is one of the best applications of good machine learning algorithm design. The XGBoost machine learning framework is one of the most well-supported in the entire industry. Because of its importance in the industry, it is a machine learning algorithm you can trust.
It is an optimizing system that can compute really well to make your decision tree more accurate. It might seem technical, but what this means is that the decision tree has a machine learning algorithm applied to it that makes it even more accurate than it was before. It then steps through the entire decision tree to make each piece of it more accurate. This makes the overall decision tree accurate, which is what extreme gradient boosting stands for.
What Is LightGBM
The trouble with XGBoost is that it isn’t the only game in town. The light gradient boosting machine was built by Microsoft to implement gradient boosting on a decision tree in a more efficient way. The big difference is that the data points it holds have larger gradients, meaning that it is a lot easier to find splits in the decision tree. That means it handles sparse data well and rarely takes non-zero values simultaneously. It reduces the number of columns in the dataset, which is one of the ways it makes the process more efficient for you.
Choosing Between XGBoost and LightGBM
Ultimately, XGBoost is an excellent machine learning algorithm for people who want an open-source library. The XGB model of doing things can work well if you are trying to implement a simple decision tree without all the added complexity. They are both flexible and accurate, but there are certain things that make one choice right for you.
If both of these machine learning algorithms are too complicated, you might want to choose the one that you think will be the easiest to learn. XGBoost is well supported in the industry, making it an easy choice because it offers many great features, like the ability to grow a decision tree in a best-first fashion. However, it should be noted that LightGBM offers the features well. Ultimately, choose based on your skill set, as they are quite similar.