From 08848ab3eeaa042bd90d2e4140f56aa08b682599 Mon Sep 17 00:00:00 2001 From: Tianqi Chen Date: Sun, 10 May 2015 17:45:20 -0700 Subject: [PATCH] Update README.md --- README.md | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 093d8294d..333bd4fa7 100644 --- a/README.md +++ b/README.md @@ -37,16 +37,15 @@ What's New Features ======== -* Sparse feature format: - - Sparse feature format allows easy handling of missing values, and improve computation efficiency. -* Push the limit on single machine: - - Efficient implementation that optimizes memory and computation. -* Speed: XGBoost is very fast - - IN [demo/higgs/speedtest.py](demo/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier -* Layout of gradient boosting algorithm to support user defined objective -* Distributed and portable - - The distributed version of xgboost is highly portable and can be used in different platforms - - It inheritates all the optimizations made in single machine mode, maximumly utilize the resources using both multi-threading and distributed computing. +* Easily accessible in python, R, Julia, CLI +* Fast speed and memory efficient + - Can be more than 10 times faster than GBM in sklearn and R + - Handles sparse matrices, support external memory +* Accurate prediction, and used extensively by data scientists and kagglers + - See [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links) +* Distributed and Portable + - The distributed version runs on Hadoop (YARN), MPI, SGE etc. + - Scales to billions of examples and beyond Build =======