What's the difference between this Python project and similar ones?
This library is solely for gradient boosting.
This resolves issue #821.
Enumerate comparisons.
--
Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/829
**Author:** [@jonmhong](https://github.com/jonmhong)
**Created:** 2/2/2017
**Status:** ✅ Merged
**Merged:** 2/2/2017
**Merged by:** [@vinta](https://github.com/vinta)
**Base:** `master` ← **Head:** `master`
---
### 📝 Commits (2)
- [`e22c3fa`](https://github.com/vinta/awesome-python/commit/e22c3fa8f4e2d2b112dc2c1df8fd420050723885) Resolve Issue #821
- [`2a07c91`](https://github.com/vinta/awesome-python/commit/2a07c9155c79b88d8407522eb74f536f5caaebc9) Resolved Issue #821
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## What is this Python project?
XGBoost is a gradient boosting library. It's one of the most popular GB libraries used among Kaggle users because it's yielded some of the highest scores. And its also a favorite with some of the champions.
Here's an example of its use with an Otto dataset: https://www.kaggle.com/tqchen/otto-group-product-classification-challenge/understanding-xgboost-model-on-otto-data
Here is a Kaggle first place team winner, showing their love for it: http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/
Describe features.
## What's the difference between this Python project and similar ones?
This library is solely for gradient boosting.
This resolves issue #821.
Enumerate comparisons.
--
Anyone who agrees with this pull request could vote for it by adding a :+1: to it, and usually, the maintainer will merge it when votes reach **20**.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/829
Author: @jonmhong
Created: 2/2/2017
Status: ✅ Merged
Merged: 2/2/2017
Merged by: @vinta
Base:
master← Head:master📝 Commits (2)
e22c3faResolve Issue [PR #913] [CLOSED] Bringing fork up to date (#821)2a07c91Resolved Issue [PR #913] [CLOSED] Bringing fork up to date (#821)📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
What is this Python project?
XGBoost is a gradient boosting library. It's one of the most popular GB libraries used among Kaggle users because it's yielded some of the highest scores. And its also a favorite with some of the champions.
Here's an example of its use with an Otto dataset: https://www.kaggle.com/tqchen/otto-group-product-classification-challenge/understanding-xgboost-model-on-otto-data
Here is a Kaggle first place team winner, showing their love for it: http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/
Describe features.
What's the difference between this Python project and similar ones?
This library is solely for gradient boosting.
This resolves issue #821.
Enumerate comparisons.
--
Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.