Boston Housing Dataset: It's a regression problem dataset which has information about a various attribute of houses in Boston and their price in dollar.We’ll be using the below-mentioned three different datasets which are available from sklearn as a part of this tutorial for explanation purposes. We'll start by importing the necessary libraries which we'll use as a part of this tutorial. We'll try to cover the majority of features available from xgboost to make this tutorial a short reference to master xgboost API. As a part of this tutorial, we'll explain the API of xgboost and its various features through different examples.
Apart from this, xgboost provides support for controlling feature interactions, custom evaluation functions, callbacks during training, monotonic constraints, etc.
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It even provides an interface to run the algorithm from the command line/shell.
#PYTHON XGBREGRESSOR OBJECTIVE CODE CODE#
Xgboost code can be run on a distributed environment like AWS YARN, Hadoop, etc. Xgboost provides API in C, C++, Python, R, Java, Julia, Ruby, and Swift. Xgboost even supports running an algorithm on GPU with simple configuration which will complete quite fast compared to when run on CPU. Currently, it has support for dask to run the algorithm in a distributed environment. The distributed algorithm can be useful if data does not fit into to main memory of the machine. It can run in parallel and distributed environments to speed up the training process.
Xgboost lets us handle a large amount of data that can have samples in billions with ease. It's designed to be quite fast compared to the implementation available in sklearn. Xgboost is a machine learning library that implements the gradient boosting trees concept.
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