XGBoost stands for eXtreme Gradient Boosting.
The name xgboost, though, actually refers to the engineering goal to push the limit of computations resources for boosted tree algorithms.
XGBoost dominates structured or tabular datasets on classification and regression predictive modeling problems.
Why Use XGBoost?
The two reasons to use XGBoost are also the two goals of any project:
Here is your chance to learn this highly in demand set of skills with a gentle introduction to the topic that leaves no stone unturned.
This training is best suited for IT, data management, and analytics professionals looking to gain expertise in AI and ML including: Software Developers and Architects, Analytics Professionals, Senior IT professionals, Testing and Mainframe Professionals, Data Management Professionals, Business Intelligence Professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics and Data Science or AI
- You need to have intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
-Intermediate statistics background. You are familiar with probability.
-Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of neural network architecture (preferrably a recurrent neural network or a long short-term memory network).
-You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.