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自己平时会使用的一些统计学和数学模型,目前有两个改进的朴素贝叶斯算法和一个TOPSIS

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# 使用说明

自己平时会使用的一些统计学和数学模型,目前有两个改进的朴素贝叶斯算法和一个TOPSIS

GitHub: [https://github.com/CheckeyZerone/Checkey-Sklearn](https://github.com/CheckeyZerone/Checkey-Sklearn)

PyPI: [https://pypi.org/project/checkey-sklearn/](https://pypi.org/project/checkey-sklearn/)

## 版权声明

Checkey-Sklearn Copyright (C) 2023 CheckeyZerone

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

## 安装方法 `commandline pip install checkey-sklearn `

## 依赖的第三方模块包

` numpy pandas scikit-learn `

## 实现算法

  • 朴素贝叶斯改进

  • 熵权-TOPSIS模型

## 使用方法

`python model = Model(*params) model.fit(x_train[, y_train, params]) model.predict(x_test) # or model.transform(x_test) `

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