<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[chenhepg的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/chenhepg</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; chenhepg]]></copyright><item><title><![CDATA[Miniconda config channels的查看、删除、添加]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/155890669</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/155890669</guid><author>chenhepg</author><pubDate>Sat, 13 Dec 2025 18:16:42 +0800</pubDate><description><![CDATA[1. 背景2. 配置文件.condarc3 --show channels 的工作机制4. 装完 Miniconda 第一件事，初始配置5. 参考连接@创建于：2025.12.13@修改于：2025.12.13。]]></description><category></category></item><item><title><![CDATA[在R的 RGui中，使用devtools 安装trajeR]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/138473851</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/138473851</guid><author>chenhepg</author><pubDate>Sun, 05 May 2024 21:56:45 +0800</pubDate><description><![CDATA[创建于：2024.5.5。]]></description><category></category></item><item><title><![CDATA[R语言中，查看经安装的包，查看已经加载的包，查看特定包是否已经安装，安装包，更新包，卸载包]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/138449454</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/138449454</guid><author>chenhepg</author><pubDate>Sat, 04 May 2024 20:56:50 +0800</pubDate><description><![CDATA[R语言中，查看经安装的包，查看已经加载的包，查看特定包是否已经安装，安装包，更新包，卸载包]]></description><category></category></item><item><title><![CDATA[Python调用R出现“UnicodeDecodeError: ‘utf-8‘ codec can‘t decode byte 0xb2” 问题【转载】]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/138011665</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/138011665</guid><author>chenhepg</author><pubDate>Sat, 20 Apr 2024 21:09:18 +0800</pubDate><description><![CDATA[纯粹是为了记录。]]></description><category></category></item><item><title><![CDATA[R version 4.1.0 安装ggplot2，options(pkgType=“binary“) install.packages(“ggplot2“)]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/138010407</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/138010407</guid><author>chenhepg</author><pubDate>Sat, 20 Apr 2024 20:45:27 +0800</pubDate><description><![CDATA[ggplot2]]></description><category></category></item><item><title><![CDATA[Python实现逐步回归]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/132939122</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/132939122</guid><author>chenhepg</author><pubDate>Sun, 17 Sep 2023 10:23:56 +0800</pubDate><description><![CDATA[逐步回归是回归分析中一种筛选变量的过程，我们可以使用逐步回归从一组候选变量中筛选起作用的变量或剔除不起作用的变量进而构建模型。逐步回归有三种筛选变量的方法。向前筛选（forward selection）: 首先挑选单独解释因变量变异最大的自变量，然后将剩余自变量逐个引入模型，引入后看该变量的加入是否使得模型发生显著性变化（F检验），如果发生了显著性变化，则将该变量引入模型中，否则忽略该变量，直至所有变量都进行了考虑。特点：自变量一旦选入，则永远保存在模型中。]]></description><category></category></item><item><title><![CDATA[ChatGLM-6B【完全转载】]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/130454751</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/130454751</guid><author>chenhepg</author><pubDate>Mon, 01 May 2023 11:20:39 +0800</pubDate><description><![CDATA[ChatGLM-6B【完全转载】]]></description><category></category></item><item><title><![CDATA[利用PyCharm：git commit后，如何撤销commit]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/130103950</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/130103950</guid><author>chenhepg</author><pubDate>Wed, 12 Apr 2023 12:14:48 +0800</pubDate><description><![CDATA[利用PyCharm：git commit后，如何撤销commit]]></description><category></category></item><item><title><![CDATA[pandas DataFrame top N rows，某列值最大的N行]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/129929156</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/129929156</guid><author>chenhepg</author><pubDate>Mon, 03 Apr 2023 14:47:48 +0800</pubDate><description><![CDATA[pandas.DataFrame   nsmallest  和 nlargest 的使用]]></description><category></category></item><item><title><![CDATA[GPT/ChatGPT相关资料]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/129663627</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/129663627</guid><author>chenhepg</author><pubDate>Mon, 20 Mar 2023 11:20:37 +0800</pubDate><description><![CDATA[GPT、ChatGPT相关的技术资料]]></description><category></category></item><item><title><![CDATA[Chi-Square Goodness of Fit Test | Hosmer–Lemeshow test]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/129650558</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/129650558</guid><author>chenhepg</author><pubDate>Sun, 19 Mar 2023 14:55:37 +0800</pubDate><description><![CDATA[Hosmer-Lemeshow test with chi-square method]]></description><category></category></item><item><title><![CDATA[利用PyCharm：git创建tag，删除tag，修改tag名称，然后同步到远端仓库]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/129498547</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/129498547</guid><author>chenhepg</author><pubDate>Mon, 13 Mar 2023 17:08:45 +0800</pubDate><description><![CDATA[git tag的添加，删除，重命名]]></description><category></category></item><item><title><![CDATA[ROC曲线 置信区间 画图展示]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/129114984</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/129114984</guid><author>chenhepg</author><pubDate>Sun, 19 Feb 2023 21:21:43 +0800</pubDate><description><![CDATA[ROC 置信区间 画图展示]]></description><category></category></item><item><title><![CDATA[python实现 AUC值的 95%置信区间]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/129114795</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/129114795</guid><author>chenhepg</author><pubDate>Sun, 19 Feb 2023 21:15:49 +0800</pubDate><description><![CDATA[AUC值的95%置信区间  三种计算方式]]></description><category></category></item><item><title><![CDATA[人工智能、机器学习和深度学习的概念、联系与区别]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/128798737</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/128798737</guid><author>chenhepg</author><pubDate>Mon, 30 Jan 2023 10:59:44 +0800</pubDate><description><![CDATA[人工智能、机器学习、深度学习的定义]]></description><category></category></item><item><title><![CDATA[OCR 技术资料]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/128785018</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/128785018</guid><author>chenhepg</author><pubDate>Sun, 29 Jan 2023 14:43:39 +0800</pubDate><description><![CDATA[OCR 理论及框架]]></description><category></category></item><item><title><![CDATA[CentOS/Red Hat 安装cuDNN]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/128543132</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/128543132</guid><author>chenhepg</author><pubDate>Wed, 04 Jan 2023 10:57:18 +0800</pubDate><description><![CDATA[在Liunux（CentOS，Red Hat）系统中，安装cuDNN。]]></description><category></category></item><item><title><![CDATA[Red Hat 安装PaddleBoBo数字人]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/128477805</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/128477805</guid><author>chenhepg</author><pubDate>Thu, 29 Dec 2022 17:18:09 +0800</pubDate><description><![CDATA[Red Hat 安装PaddleBoBo数字人]]></description><category></category></item><item><title><![CDATA[logistic回归的标准化回归系数 计算方式]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/128357150</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/128357150</guid><author>chenhepg</author><pubDate>Sat, 17 Dec 2022 21:15:05 +0800</pubDate><description><![CDATA[logistic回归的标准化回归系数 ]]></description><category></category></item><item><title><![CDATA[python 的statsmodels包，计算Wald tests]]></title><link>https://blog.youkuaiyun.com/chenhepg/article/details/128357022</link><guid>https://blog.youkuaiyun.com/chenhepg/article/details/128357022</guid><author>chenhepg</author><pubDate>Sat, 17 Dec 2022 20:44:40 +0800</pubDate><description><![CDATA[python 的statsmodels包，计算Wald tests]]></description><category></category></item></channel></rss>