XGBoost stands for Extreme Gradient Boost and is developed on a gradient-boost framework. It is an open-source, scalable, and distributed gradient-boosted decision tree (GBDT) machine learning library offering high-performance implementations of gradient-boosted decision trees.
Also, the core feature of XGBoost in Python is that it provides parallel tree boosting. It is a leading machine learning library for classification, regression, and other ranking problems.
And XGBoost has become the go-to library for winning many Kaggle competitions.
Why You Should Use XGBoost in Python
XGBoost is one of the libraries that has gained significant favor over the last few years due to helping teams and individuals win virtually every Kaggle structured data competition. Researchers and companies post data that data miners and statisticians compete to produce new reliable models to predict and describe data accurately.
Initially, only R and Python supported XGBoost, but it gradually got more famous for its features. Today, different languages like Scala, Perl, Julia, Java, and many other languages support XGBoost.
These are a few of the core features behind the success and popularity of XGBoost:
- Speed and performance
- Core algorithm is parallelizable
- Consistently outperforms other algorithm methods
- Wide variety of tuning parameters
Install XGBoost in Python
To install XGBoost in Python, we must first install the package or library into your local environment.
Go to your command-line interface/terminal and write the following command:
pip install xgboost // or pip3 install xgboost
For some people,
pip works, but if it doesn’t work with your machine, you can use
pip3, and the remaining part is similar.
You can utilize the following command using
conda install -c conda-forge py-xgboost
This command will install the XGBoost onto your local machine, and then you can import it into your program and use it accordingly.
import xgboost as xgb
This will import
xgb in your program.
But make sure to install it before importing it; otherwise, it will not work. Installation of XGBoost is a prerequisite.