From cbdcbfc49c63c8c0201b429839e8b64c6a81ef52 Mon Sep 17 00:00:00 2001 From: Ajinkya Kale Date: Sat, 25 Jul 2015 12:46:28 -0700 Subject: [PATCH] some more changes to remove redundant information --- README.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 7a4cfa4c8..18c5b77c1 100644 --- a/README.md +++ b/README.md @@ -28,17 +28,17 @@ What's New ---------- * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) - - Check out the winning solution at [Highlight links](doc/README.md#highlight-links) + Check out the [winning solution](doc/README.md#highlight-links) * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) * XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing) - - Check out the winning solution at [Highlight links](doc/README.md#highlight-links) + Check out the [winning solution](doc/README.md#highlight-links) * [External Memory Version](doc/external_memory.md) Version ------- -* Current version xgboost-0.4, a lot improvment has been made since 0.3 - - Change log in [CHANGES.md](CHANGES.md) +* Current version xgboost-0.4 + - [Change log](CHANGES.md) - This version is compatible with 0.3x versions Features @@ -48,8 +48,7 @@ Features * Fast and memory efficient - Can be more than 10 times faster than GBM in sklearn and R. [benchm-ml numbers](https://github.com/szilard/benchm-ml) - 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) +* Accurate prediction, and used extensively by data scientists and kagglers - [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 @@ -75,5 +74,5 @@ License XGBoost in Graphlab Create -------------------------- -* XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the Graphlab Create in http://graphlab.com/products/create/quick-start-guide.html +* XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the [Graphlab Create](http://graphlab.com/products/create/quick-start-guide.html) * Nice blogpost by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand