Learn Data Science using Python

Another post starts with you beautiful people! XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. Recently XGBoost is released with it's newer version 1.0.0 which has improvements like performance scaling for multi core CPUs, improved installation experience on Mac OSX, availability of distributed XGBoost on Kubernates etc. In this post we are going to explore it's multi processing capabilities on a real world ml problem  Otto Group Product Classification Challenge . In the end of the post I will share my kaggle kernel link also so that you can explore my complete code. Once you go to the challenge link in Kaggle and start your kernel, first you need to enable the Internet option in the noteb...