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A Guide to XGBoost in Python - A site aimed at building a Data Science, Artificial Intelligence and Machine Learning empire.

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In this article, we will take a look at the various aspects of the XGBoost library. XGBoost is one of the most reliable machine learning libraries when dealing with huge datasets. In my previous article, I gave a brief introduction about XGBoost on how to use it. This article will mainly aim towards exploring many of the useful features of XGBoost. When using machine learning libraries, it is not only about building state-of-the-art models.


Effective Data Storytelling for Larger-than-Memory Datasets

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Streamlit enables data scientists to build lightweight, intuitive web applications without writing any frontend code. You don't even have to leave the friendly confines of Python; it's that good;) That said, working with a front-end solution like Streamlit can become cumbersome when the size of your dataset or computation increases beyond something that completes within a few seconds. It's likely that you're using Streamlit in the first place because you want to create a smoother, more intuitive user experience. And my guess is that having your user sit around for minutes (or hours!) while a computation runs doesn't make it into your definition of "smooth"… You could, of course, choose to pay for and manage expensive clusters of machines (either locally or on the cloud). But unless your Streamlit app is as popular as Netflix, it's likely that your cluster will sit idle for long periods of time.


XGBoost: Implementing the Winningest Kaggle Algorithm in Spark and Flink

@machinelearnbot

XGBoost is a library designed and optimized for tree boosting. Gradient boosting trees model is originally proposed by Friedman et al. By embracing multi-threads and introducing regularization, XGBoost delivers higher computational power and more accurate prediction. More than half of the winning solutions in machine learning challenges hosted at Kaggle adopt XGBoost (Incomplete list). XGBoost has provided native interfaces for C, R, python, Julia and Java users.


XGBoost4J: Portable Distributed XGBoost in Spark, Flink and Dataflow

#artificialintelligence

XGBoost is a library designed and optimized for tree boosting. Gradient boosting trees model is originally proposed by Friedman et al. By embracing multi-threads and introducing regularization, XGBoost delivers higher computational power and more accurate prediction. More than half of the winning solutions in machine learning challenges hosted at Kaggle adopt XGBoost (Incomplete list). XGBoost has provided native interfaces for C, R, python, Julia and Java users.


Lightning Fast XGBoost on Multiple GPUs

#artificialintelligence

XGBoost is one of the most used libraries fora data science. At the time XGBoost came into existence, it was lightning fast compared to its nearest rival Python's Scikit-learn GBM. But as the times have progressed, it has been rivaled by some awesome libraries like LightGBM and Catboost, both on speed as well as accuracy. I, for one, use LightGBM for most of the use cases where I have just got CPU for training. But when I have a GPU or multiple GPUs at my disposal, I still love to train with XGBoost.