Every day we hear about the breakthroughs in artificial intelligence. However, have you wondered what challenges it faces?
Challenges occur in highly unstructured data like DNA sequencing, credit card transactions, and even in cybersecurity, which is the backbone of keeping our online presence safe from fraudsters. Does this thought make you yearn to know more about the science and reasoning behind these systems? Do not worry! We’ve got you covered. In the cyber era, machine learning (ML) has provided us with the solutions to these problems with the implementation of Gradient Boosting Machines (GBM). We have ample algorithms to choose from to do gradient boosting for our training data but still, we encounter different issues like poor accuracy, high loss, large variance in the result.
Here, we are going to introduce you to a state of the art machine learning algorithm XGBoost built by Tianqi Chen, that will not only overcome the issues but also perform exceptionally well for regression and classification problems. This blog will help you discover the insights, techniques, and skills with XGBoost that you can then bring to your machine learning projects.
