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DOC update documentation for v0.1.4
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‎README.md‎

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Imbalanced Ensemble: <i>ensemble learning for class-imbalanced data in Python.</i>
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<br>
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[<a href="https://imbalanced-ensemble.readthedocs.io/">Documentation</a>]
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[<a href="https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#">Example Gallery</a>]
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[<a href="https://pypi.org/project/imbalanced-ensemble/">PyPI</a>]
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[<a href="https://imbalanced-ensemble.readthedocs.io/en/latest/release_history.html">Changelog</a>]
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[<a href="https://github.com/ZhiningLiu1998/imbalanced-ensemble/tree/main/imbalanced_ensemble">Source</a>]
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***imbalanced-ensemble* (IMBENS, imported as `imbalanced_ensemble`)** is a Python toolbox for quick implementing and deploying ensemble learning algorithms on class-imbalanced data.
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The problem of learning from imbalanced data is known as [imbalanced learning](https://github.com/ZhiningLiu1998/awesome-imbalanced-learning) or long-tail learning (under multi-class scenario).
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Currently (v0.1.3, 2021/06), IMBENS includes more than 15 ensemble imbalanced learning algorithms, from the classical SMOTEBoost (2003), RUSBoost (2010) to recent SPE (2020), from resampling to cost-sensitive learning. More algorithms will be included in the future. We also provide detailed documentation and examples across various algorithms.
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Currently (v0.1.4, 2021/06), IMBENS includes more than 15 ensemble imbalanced learning algorithms, from the classical SMOTEBoost (2003), RUSBoost (2010) to recent SPE (2020), from resampling to cost-sensitive learning. More algorithms will be included in the future. We also provide detailed documentation and examples across various algorithms.
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Read more at: [[知乎/Zhihu](https://zhuanlan.zhihu.com/p/376572330)] [[中文README](https://github.com/ZhiningLiu1998/imbalanced-ensemble/blob/main/docs/README_CN.md)].
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**Read more at: [[知乎/Zhihu](https://zhuanlan.zhihu.com/p/376572330)] [[中文README](https://github.com/ZhiningLiu1998/imbalanced-ensemble/blob/main/docs/README_CN.md)].**
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**IMBENS is featured for:**
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- &#x1F34E; **Unified, easy-to-use APIs, detailed documentation and examples.**
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- &#x1F34E; **Unified, easy-to-use APIs, detailed [documentation](https://imbalanced-ensemble.readthedocs.io/) and [examples](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#).**
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- &#x1F34E; **Capable for *multi-class* imbalanced learning out-of-box.**
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- &#x1F34E; **Optimized performance with parallelization when possible using [joblib](https://github.com/joblib/joblib).**
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- &#x1F34E; **Powerful, customizable, interactive training logging and visualizer.**
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## 5-min Quick Start with IMBENS
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**Here, we provide some quick guides to help you get started with IMBENS.**
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**We strongly encourage users to check out the [**example gallery**](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#) for more comprehensive usage examples, which demonstrate many advanced features of IMBENS.**
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![](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/imbalanced-ensemble/example_gallery_snapshot.png)
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### A minimal working example
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Taking self-paced ensemble [1] as an example, it only requires less than 10 lines of code to deploy it:

‎docs/README_CN.md‎

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#### 链接：[[Github](https://github.com/ZhiningLiu1998/imbalanced-ensemble)] [[API/文档/使用手册](https://imbalanced-ensemble.readthedocs.io/)] [[PyPI](https://pypi.org/project/imbalanced-ensemble/)] [[版本历史](https://imbalanced-ensemble.readthedocs.io/en/latest/release_history.html)] [[源代码](https://github.com/ZhiningLiu1998/imbalanced-ensemble/tree/main/imbalanced_ensemble)] [[下载](https://pypi.org/project/imbalanced-ensemble/#files)]
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#### 链接：[[Github](https://github.com/ZhiningLiu1998/imbalanced-ensemble)] [[API/文档/使用手册](https://imbalanced-ensemble.readthedocs.io/)] [[示例仓库](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#)] [[PyPI](https://pypi.org/project/imbalanced-ensemble/)] [[版本历史](https://imbalanced-ensemble.readthedocs.io/en/latest/release_history.html)] [[源代码](https://github.com/ZhiningLiu1998/imbalanced-ensemble/tree/main/imbalanced_ensemble)] [[下载](https://pypi.org/project/imbalanced-ensemble/#files)]
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**imbalanced-ensemble（IMBENS）是一个 Python 库/软件包。它主要用于在类别不平衡数据上快速实现和部署集成学习算法。截至目前（v0.1.3, 2021/06/08），IMBENS已实现了14种不同的不平衡集成学习算法，从经典的SMOTEBoost (2003) 到最近的 SPE (2020)，从欠采样、过采样到代价敏感学习，全部包括在内。IMBENS实现的大部分方法都具有详细的 [文档和使用手册](https://imbalanced-ensemble.readthedocs.io/)，并将在未来继续更新加入其他方法。**
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**imbalanced-ensemble（IMBENS）是一个 Python 库/软件包。它主要用于在类别不平衡数据上快速实现和部署集成学习算法。截至目前（2021/06），IMBENS已实现了14种不同的不平衡集成学习算法，从经典的SMOTEBoost (2003) 到最近的 SPE (2020)，从欠采样、过采样到代价敏感学习，全部包括在内。IMBENS实现的大部分方法都具有详细的 [文档和使用手册](https://imbalanced-ensemble.readthedocs.io/)，并将在未来继续更新加入其他方法。**
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**欢迎 star / issue / PR !**
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**更多相关: [[知乎/Zhihu](https://zhuanlan.zhihu.com/p/376572330)] [[英文README](https://github.com/ZhiningLiu1998/imbalanced-ensemble#readme)].**
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**IMBENS的主要特性有：**
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- &#x1F34E; **统一易用的API设计，便于使用和二次开发**
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- &#x1F34E; **统一易用的API设计，便于使用和二次开发，详细的 [文档](https://imbalanced-ensemble.readthedocs.io/) 和 [示例](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#)**
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- &#x1F34E; **所有实现的方法均原生支持多分类不平衡问题**
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- &#x1F34E; **在可能的情况下，使用 [joblib](https://github.com/joblib/joblib) 实现并行训练/预测以优化性能**
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- &#x1F34E; **强大的、可定制的、交互式的模型训练日志记录和可视化工具**
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## 5分钟快速上手IMBENS
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**本节中我们提供一些简单的指南来帮助您快速开始使用 IMBENS。**
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**我们强烈希望您查看 [**示例仓库**](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#) 中的更全面的使用示例，其中演示了 IMBENS 的许多高级特性。**
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![](https://raw.githubusercontent.com/ZhiningLiu1998/figures/master/imbalanced-ensemble/example_gallery_snapshot.png)
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#### 基础示例
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一个可运行的示例：以 SPE[1] 为例，仅需少于10行的代码就可以部署它：

‎docs/source/_static/css/my_theme.css‎

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‎docs/source/_static/thumbnail.png‎

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docs/source/api/ensemble/_autosummary/imbalanced_ensemble.ensemble.reweighting.AdaCostClassifier.rst renamed to docs/source/api/ensemble/_autosummary/imbalanced_ensemble.ensemble.AdaCostClassifier.rst

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AdaCostClassifier
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AdaUBoostClassifier
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AsymBoostClassifier
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BalanceCascadeClassifier
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BalancedRandomForestClassifier
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