<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[appke的专栏]]></title><description><![CDATA[微信公众号：CN机器学习]]></description><link>https://blog.youkuaiyun.com/oppo603</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; oppo603]]></copyright><item><title><![CDATA[12.房价预测集成学习Stacking Learning]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/102555068</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/102555068</guid><author>oppo603</author><pubDate>Tue, 15 Oct 2019 08:42:30 +0800</pubDate><description><![CDATA[洛杉矶房价预测，集成学习，stacking Learning]]></description><category></category></item><item><title><![CDATA[11.房价预测多项式回归]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/102385356</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/102385356</guid><author>oppo603</author><pubDate>Mon, 14 Oct 2019 19:48:46 +0800</pubDate><description><![CDATA[洛杉矶房价预测，多项式回归]]></description><category></category></item><item><title><![CDATA[10.手写线性回归模型L1和L2正则化]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/102385066</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/102385066</guid><author>oppo603</author><pubDate>Wed, 09 Oct 2019 15:46:44 +0800</pubDate><description><![CDATA[手写线性回归]]></description><category></category></item><item><title><![CDATA[9.最小二乘法解方程方式和矩阵方式]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/101782790</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/101782790</guid><author>oppo603</author><pubDate>Wed, 09 Oct 2019 15:43:15 +0800</pubDate><description><![CDATA[线性回归最小二乘法，解方程，向量方法]]></description><category></category></item><item><title><![CDATA[8.房价预测基础线性回归]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100799355</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100799355</guid><author>oppo603</author><pubDate>Mon, 30 Sep 2019 17:41:14 +0800</pubDate><description><![CDATA[房价预测，基础线性回归]]></description><category></category></item><item><title><![CDATA[7.房价预测新一轮空值填充]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100561324</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100561324</guid><author>oppo603</author><pubDate>Fri, 13 Sep 2019 11:12:52 +0800</pubDate><description><![CDATA[特征之间的关系，新一轮空值填充]]></description><category></category></item><item><title><![CDATA[6.洛杉矶房价相关性分析]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100555905</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100555905</guid><author>oppo603</author><pubDate>Thu, 05 Sep 2019 16:41:53 +0800</pubDate><description><![CDATA[相关性分析，皮尔森系数，图像表示]]></description><category></category></item><item><title><![CDATA[5.Seaborn数据分析]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100555390</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100555390</guid><author>oppo603</author><pubDate>Thu, 05 Sep 2019 11:57:18 +0800</pubDate><description><![CDATA[Seaborn数据分析，蜂群图，带状图，箱线图，提琴图]]></description><category></category></item><item><title><![CDATA[4.Pyplot数据快查表]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100154633</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100154633</guid><author>oppo603</author><pubDate>Fri, 30 Aug 2019 13:09:06 +0800</pubDate><description><![CDATA[Matplotlib数据快查表，python画图，pyplot]]></description><category></category></item><item><title><![CDATA[3.洛杉矶房价预测的Baseline]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100133613</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100133613</guid><author>oppo603</author><pubDate>Thu, 29 Aug 2019 20:22:14 +0800</pubDate><description><![CDATA[洛杉矶房价预测Baseline]]></description><category></category></item><item><title><![CDATA[2.Pandas操作快查表]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100133269</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100133269</guid><author>oppo603</author><pubDate>Thu, 29 Aug 2019 10:49:26 +0800</pubDate><description><![CDATA[Pandas操作快查表]]></description><category></category></item><item><title><![CDATA[1.人工智能的Pipeline]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/100133241</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/100133241</guid><author>oppo603</author><pubDate>Thu, 29 Aug 2019 10:48:42 +0800</pubDate><description><![CDATA[人工智能的Pipeline]]></description><category></category></item><item><title><![CDATA[sklearn中回归问题的评估方法]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/86570450</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/86570450</guid><author>oppo603</author><pubDate>Mon, 21 Jan 2019 10:47:28 +0800</pubDate><description><![CDATA[在对回归问题的建模分析中，经常会遇到对回归问题的评估问题，如何评估回归模型的优劣呢，本文整理了sklearn中的metrics中关于回归问题的评估方法。
首先导入相应的函数库并建立模型
#导入相应的函数库
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.en...]]></description><category></category></item><item><title><![CDATA[经典机器学习算法的优缺点]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/86181829</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/86181829</guid><author>oppo603</author><pubDate>Wed, 09 Jan 2019 21:42:12 +0800</pubDate><description><![CDATA[经典机器学习算法的优缺点
作者: orangeshushu分类: ML &amp;amp; DL发布时间: 2018-07-12 11:42
kNN

特点：k值的选择会对k近邻法的结果产生重大影响
优点：精度高、对异常值不敏感、无数据输入假定
缺点：计算复杂度高、空间复杂度高
适用数据范围：数值型和标称型

朴素贝叶斯

优点：学习和预测的效率高，且易于实现；在数据较少的情况下仍然有效，可以处理多类别问...]]></description><category></category></item><item><title><![CDATA[洛杉矶房价预测-数据快查表]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/85331392</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/85331392</guid><author>oppo603</author><pubDate>Fri, 28 Dec 2018 20:57:05 +0800</pubDate><description><![CDATA[洛杉矶房价预测，数据快查表]]></description><category></category></item><item><title><![CDATA[elasticsearch集成ik分词器]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/85123929</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/85123929</guid><author>oppo603</author><pubDate>Thu, 20 Dec 2018 15:38:46 +0800</pubDate><description><![CDATA[一．下载编译
从地址https://github.com/medcl/elasticsearch-analysis-ik下载elasticsearch中文分词器

这里默认的是master的  但是master的项目需要用gradle编译，这里选择1.8.0版本。而且从下面的介绍可以知道1.8.0正好对应elasticsearch的2.2.0版本
下载后的压缩包解压后进去发现是pom工程
分别执行...]]></description><category></category></item><item><title><![CDATA[Scala学习笔记导航]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/84942027</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/84942027</guid><author>oppo603</author><pubDate>Mon, 10 Dec 2018 16:25:42 +0800</pubDate><description><![CDATA[
Scala入门笔记
Scala类和对象
Scala函数
Scala字符串和Trait特征
Scala集合
Scala模式匹配
Scala的Actor模型


]]></description><category></category></item><item><title><![CDATA[Python实例集锦]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/84074573</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/84074573</guid><author>oppo603</author><pubDate>Wed, 14 Nov 2018 21:38:02 +0800</pubDate><description><![CDATA[Python实例集锦
Python实例之一
有四个数字：1、2、3、4，能组成多少个互不相同且无重复数字的三位数？各是多少？
使用三次循环
for x in range(1,5):
    for y in range(1,5):
        for z in range(1,5):
            if (x != y) and (x != z) and (y != z):
    ...]]></description><category></category></item><item><title><![CDATA[mysql数据库基本操作]]></title><link>https://blog.youkuaiyun.com/oppo603/article/details/84071400</link><guid>https://blog.youkuaiyun.com/oppo603/article/details/84071400</guid><author>oppo603</author><pubDate>Wed, 14 Nov 2018 18:18:03 +0800</pubDate><description><![CDATA[1、数据库（database）管理
1.1 create 创建数据库
createdatabase firstDB;

1.2 show 查看所有数据库
mysql&amp;amp;amp;amp;gt; show databases;
+--------------------+
| Database           |
+--------------------+
| information_schema |
| fir...]]></description><category></category></item></channel></rss>