From d120167725fd7d56ab84bf7feb479389b8866eab Mon Sep 17 00:00:00 2001 From: Will Stanton Date: Wed, 22 Jul 2015 09:19:22 -0600 Subject: [PATCH] Fixed a few typos in README --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 4a5e7bf6e..21d15ce56 100644 --- a/README.md +++ b/README.md @@ -6,11 +6,11 @@ DMLC/XGBoost An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data -Checkout our [Comitters and Contributors](CONTRIBUTORS.md) who keep make xgboost better. +Checkout our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better. -Documentations: [Documentation of dmlc/xgboost](doc/README.md) +Documentation: [Documentation of dmlc/xgboost](doc/README.md) -Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion) +Issue Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion) Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost. - Use issue tracker for bug reports, feature requests etc. @@ -30,13 +30,13 @@ What's New - Checkout the winning solution at [Highlight links](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) - - Checkout the winning solution at [Highlight links](doc/README.md#highlight-links) + - Check out the winning solution at [Highlight links](doc/README.md#highlight-links) * [External Memory Version](doc/external_memory.md) Contributing to XGBoost ========= -XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users. -* Checkout [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something. +XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users. +* Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something. * Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users. * Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged. @@ -66,5 +66,5 @@ Version 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 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 -* Nice blogpost by Jay Gu using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand +* 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 +* 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