What is Xgboost?
Xgboost is a machine learning algorithm that is used for predictive modeling. It was developed by Tianqi Chen and Guodong Ji in 2013 and has been widely used in the field of data science.
XGBoost is a decision treebased ensemble Machine Learning algorithm that uses an object-oriented programming abstract machine. It can be applied to unstructured data and tends to outperform other algorithms on prediction problems like these. The best choices for algorithm to use on small-to-medium structured/tabular data is the decision treebased algorithms. See the graph below for how they’ve evolved over the years.
The algorithm is different because of the following factors:
- A wide range of applications: When it comes to regression, classification, ranking and user-defined prediction problems there are many different tools for solving. A lot of these tools are portable and can be used on Windows, Linux or OS X.
- Portability: A lot of these tools are portable and can be used on Windows, Linux or OS X.
- Languages: Supports all major programming languages including C++, Python, R, Java, Scala, and Julia.
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