With our powers combined! xgboost and pipelearner • blogR

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So bringing them together will make for an awesome combination! Let's work out how to deal with this. To follow this post you'll need the following packages: Our example will be to try and predict whether tumours are cancerous or not using the Breast Cancer Wisconsin (Diagnostic) Data Set. For this example, we'll use pipelearner to perform a grid search of some xgboost hyperparameters. Grid searching is easy with pipelearner.


A Gentle Introduction to XGBoost for Applied Machine Learning - Machine Learning Mastery

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When getting started with a new tool like XGBoost, it can be helpful to review a few talks on the topic before diving into the code. Tianqi Chen, the creator of the library gave a talk to the LA Data Science group in June 2016 titled "XGBoost: A Scalable Tree Boosting System". There is more information on the DataScience LA blog. Tong He, a contributor to XGBoost for the R interface gave a talk at the NYC Data Science Academy in December 2015 titled "XGBoost: eXtreme Gradient Boosting". There is more information about this talk on the NYC Data Science Academy blog.


XGBoost: Implementing the Winningest Kaggle Algorithm in Spark and Flink

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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

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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.


XGBoost With Python - Machine Learning Mastery

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XGBoost is the dominant technique for predictive modeling on regular data. The gradient boosting algorithm has proven to be one of the top techniques on a wide range of predictive modeling problems, and the XGBoost implementation has proven to be the fastest available for use in applied machine learning. When asked, the best machine learning competitors in the world recommend using XGBoost. In this new Ebook written in the friendly Machine Learning Mastery style that you're used to, learn exactly how to get started and bring XGBoost to your own machine learning projects. The Gradient Boosting algorithm has been around since 1999.