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禾武器 | 基于K-means算法逻辑的自动分箱

发布于 2021-08-11

<section data-role="outer" label="Powered by 135editor.com" data-mpa-powered-by="yiban.io"><p style="text-align:left;max-width: 100%;min-height: 1em;letter-spacing: 0.5px;font-family: -apple-system-font, BlinkMacSystemFont, Arial, sans-serif;box-sizing: border-box !important;overflow-wrap: break-word !important;"><img class="rich_pages" data-cropselx1="0" data-cropselx2="208" data-cropsely1="0" data-cropsely2="412" data-ratio="0.9441786283891547" data-s="300,640" data-src="https://mmbiz.qpic.cn/mmbiz_gif/AJkYaTAujIxTQnOUctDo6I3vyLQgJ2JLBl9msBkTgtqMJAKN5aZ3R9XJRMtCeIGjOs0RM2LCAH5vdaF8GOnohA/640?wx_fmt=gif" data-type="gif" data-w="627" height="306" style="box-sizing: border-box;height: 306px;width: 324px;overflow-wrap: break-word !important;visibility: visible !important;" title="禾武器-黑.gif" width="324" /></p><section style="margin-right: 8px;margin-left: 8px;max-width: 100%;box-sizing: border-box;letter-spacing: 0.544px;background-color: rgb(255, 255, 255);text-align: center;font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, Arial, sans-serif;overflow-wrap: break-word !important;"><section data-tools="135编辑器" data-id="101991"><section style="margin: 10px auto;"><section style="display: flex;justify-content: flex-start;align-items: center;margin-top: 1em;flex-direction: row-reverse;"><br /></section></section></section><section style="margin-right: 8px;margin-left: 8px;max-width: 100%;letter-spacing: 0.544px;min-height: 1em;caret-color: rgb(51, 51, 51);text-size-adjust: auto;font-family: -apple-system-font, BlinkMacSystemFont, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section style="margin-top: 10px;margin-bottom: 10px;max-width: 100%;box-sizing: border-box;color: rgb(0, 0, 0);font-size: 13px;letter-spacing: 1px;font-family: -apple-system, BlinkMacSystemFont, 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&quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;overflow-wrap: break-word !important;"><section style="max-inline-size: 100%;box-sizing: border-box;color: rgb(51, 51, 51);font-size: 17px;letter-spacing: 0.544px;caret-color: rgb(255, 0, 0);max-width: 100%;line-height: 2em;margin-left: 16px;margin-right: 16px;outline: none 0px !important;overflow-wrap: break-word !important;"><strong style="max-inline-size: 100%;cursor: text;max-width: 100%;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><span style="max-inline-size: 100%;cursor: text;max-width: 100%;background-color: rgb(0, 0, 0);color: rgb(255, 255, 255);box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><em style="max-inline-size: 100%;cursor: text;max-width: 100%;color: rgb(51, 51, 51);font-size: 20px;letter-spacing: 0.544px;background-color: rgb(255, 255, 255);font-family: -apple-system-font, system-ui, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><span style="max-inline-size: 100%;cursor: text;max-width: 100%;letter-spacing: 0.544px;color: rgb(178, 178, 178);font-family: -apple-system-font, BlinkMacSystemFont, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><strong style="max-inline-size: 100%;cursor: text;max-width: 100%;color: rgb(53, 53, 53);font-size: 12px;letter-spacing: 0.544px;font-family: &quot;Helvetica Neue&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei&quot;, 黑体, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><strong style="max-inline-size: 100%;cursor: text;max-width: 100%;font-family: -apple-system-font, BlinkMacSystemFont, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><span style="max-inline-size: 100%;cursor: text;max-width: 100%;font-size: 20px;text-transform: uppercase;letter-spacing: 1px;font-family: didot;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;">&nbsp;</span></strong></strong></span></em></span></strong><span style="color: rgb(178, 178, 178);"><strong style="max-inline-size: 100%;cursor: text;max-width: 100%;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><span style="color: rgb(178, 178, 178);max-inline-size: 100%;cursor: text;max-width: 100%;background-color: rgb(0, 0, 0);box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><em style="max-inline-size: 100%;cursor: text;max-width: 100%;color: rgb(51, 51, 51);font-size: 20px;letter-spacing: 0.544px;background-color: rgb(255, 255, 255);font-family: -apple-system-font, system-ui, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><span style="color: rgb(178, 178, 178);max-inline-size: 100%;cursor: text;max-width: 100%;letter-spacing: 0.544px;font-family: -apple-system-font, BlinkMacSystemFont, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><strong style="max-inline-size: 100%;cursor: text;max-width: 100%;color: rgb(53, 53, 53);font-size: 12px;letter-spacing: 0.544px;font-family: &quot;Helvetica Neue&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei&quot;, 黑体, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><strong style="max-inline-size: 100%;cursor: text;max-width: 100%;font-family: -apple-system-font, BlinkMacSystemFont, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;"><span style="color: rgb(178, 178, 178);max-inline-size: 100%;cursor: text;max-width: 100%;font-size: 20px;text-transform: uppercase;letter-spacing: 1px;font-family: didot;box-sizing: border-box !important;outline: none 0px !important;overflow-wrap: break-word !important;">&nbsp; /&nbsp;INTRODUCTION /</span></strong></strong></span></em></span></strong></span></section></section><section style="max-width: 100%;box-sizing: border-box;color: rgb(0, 0, 0);font-size: 13px;letter-spacing: 1px;font-family: -apple-system, BlinkMacSystemFont, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;overflow-wrap: break-word !important;"><section style="max-width: 100%;box-sizing: border-box;min-height: 1em;margin-left: 16px;margin-right: 16px;overflow-wrap: break-word !important;"><br /></section></section></section></section></section><section data-mpa-template="t" mpa-from-tpl="t"><section style="box-sizing: border-box;" powered-by="xiumi.us" mpa-from-tpl="t"><section style="margin: 10px 16px;text-align: center;box-sizing: border-box;"><section style="display: inline-block;vertical-align: top;box-sizing: border-box;" mpa-from-tpl="t"><section style="margin-bottom: -6px;line-height: 1em;padding-left: 2px;padding-right: 2px;font-size: 18px;color: rgb(72, 64, 64);box-sizing: border-box;" mpa-from-tpl="t"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;" mpa-from-tpl="t" mpa-is-content="t">一、什么是“分箱”</strong></p></section><section style="width: 100%;height: 10px;background-color: rgb(255, 202, 0);box-sizing: border-box;" mpa-from-tpl="t"><br /></section></section></section></section></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">各行各业的打工人,经常会面对一种令人尴尬的质疑:为什么你把15-25归为一类,10-20不行吗?13-23不行吗?</span></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">以及地产干饭人经常会被问到的:120-140㎡是主力,那119㎡行不行,118㎡呢?这个时候,通常大部分人会说那样也行。</span></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">可是那样真的行吗?吾不以为然也。而且,不但那样不行,这样也不是很行。提出问题,自然也要解决问题,下面就让我们一起来看看,怎样才算是真的行。</span></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">当我们在把一组连续的数据,进行分段研究的时候,这就是“分箱”。</span></strong></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">意思是把一些初始元素,放到不同的“箱子”里去,用于增强数据的粒度,便于研究。比如把月薪水平分为:5000以下、5000-8000、8000-10000、10000-20000、20000以上。</span><span style="color: rgb(136, 136, 136);font-size: 14px;">有时候分箱也可以进行调整和改变,比如薪酬也可以直接分成“8000以上”和“8000以下”。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">同样,性别既可以分为4个箱:“男”、“女”、“无性别”、“双性别”;也可以分为2个箱:“性别明确”和“性别不明确”。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">那么问题就来了,到底分成几个箱,以及按照怎样的标准来划分才更合适呢?</span></strong></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;"><br /></span></strong></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">首先,最简单的是等距分箱、等频分箱以及自定义分箱。</span></strong></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">所谓</span><span style="font-size: 14px;color: rgb(0, 0, 0);"><strong>等距分箱</strong></span><span style="font-size: 14px;color: rgb(136, 136, 136);">,就是隔着同样的距离把整体切分。比如将薪酬按照1000-2000、2000-3000、3000-4000这样每隔1000分一段。</span><span style="color: rgb(136, 136, 136);font-size: 14px;">亦或者将房地产产品面积按照每隔10平米划分为80-90、90-100、100-110、110-120。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">而</span><span style="font-size: 14px;color: rgb(0, 0, 0);"><strong>等频分箱</strong></span><span style="font-size: 14px;color: rgb(136, 136, 136);">,就是把整体每隔n个元素放在一起作为一个箱,比如一共有1000个样本,我们按照从小到大的顺序排序后,把前250个作为第一类。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">自定义分箱</span></strong></span><span style="font-size: 14px;color: rgb(136, 136, 136);">就是通过经验和判断进行划分,或者说就是拍脑袋的。就比如我们把月薪分为了5000以下、5000-8000、8000-10000、10000-20000。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">对于有些情况,自定义分箱是个不错的选择,有时候自定义分箱,也可以在其他分箱的基础上,来补充一些数据上无法体现的东西。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">比如我们的模型里有“年龄”这一维度的数据,而研究目标是青少年的犯罪率,那么就应该适当地把年龄分为14岁以下、14-16岁、16岁以上(因为刑法规定14岁以下完全不承担任何刑事责任!)。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">当然也有用各种各样有监督的无监督的手段来分箱的,都可以进行尝试。但</span><span style="font-size: 14px;color: rgb(0, 0, 0);"><strong>今天我们要说的是一个纯粹根据数据分布规律进行分箱的策略——K-means分箱。</strong></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br mpa-from-tpl="t" /></span></section><section data-mpa-template="t" mpa-from-tpl="t"><section style="box-sizing: border-box;" powered-by="xiumi.us" mpa-from-tpl="t"><section style="margin: 10px 16px;text-align: center;box-sizing: border-box;"><section style="display: inline-block;vertical-align: top;box-sizing: border-box;" mpa-from-tpl="t"><section style="margin-bottom: -6px;line-height: 1em;padding-left: 2px;padding-right: 2px;font-size: 18px;color: rgb(72, 64, 64);box-sizing: border-box;" mpa-from-tpl="t"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;" mpa-from-tpl="t">二、K-means分箱算法</strong></p></section><section style="width: 100%;height: 10px;background-color: rgb(255, 202, 0);box-sizing: border-box;" mpa-from-tpl="t"><br /></section></section></section></section></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">K-means即K均值聚类算法,如果用过聚类的朋友们肯定就对它很熟悉了,这是最简单有效的聚类方式之一。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">首先需要指定一个大于1的正整数K,然后随机选取K个元素作为整体的聚类中心,随后计算每个对象与各聚类中心的几何距离,用于把每个对象分配给不同的聚类中心,再计算形成的每个簇的新几何中心作为新的聚类中心,重复这一过程直到每个聚类中心不再变化。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">K-means算法最大的优点,是它可以对于任意大于1的正整数K,根据数据的几何分布,来把数据划分成几类</span></strong></span><span style="font-size: 14px;color: rgb(136, 136, 136);">,显然K越大,分类效果越好,但为了避免过拟合的情况出现,我们也可以人为规定一个K,或者是K的上限,也可以通过一个判断来选择最优的K是多少。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">对于K-means算法,在一维数组中也是可以用的,当然在多维度空间中也是可行的(就是统计学常用的K-means聚类,顺便说一下这也是通常K-means聚类不可以用于逻辑变量的原因,不要再乱用了同学们)。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">大部分数据分析软件都有K-means现成的包,笔者认为相比于python和R, Matlab在数据处理上总是更胜一筹,最后也会给大家分享案例实现的代码。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">K-means的核心在于,通过计算机去寻找数据的聚集点,从而在分箱的同时,避免了过多的信息丢失。</span></strong></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br mpa-from-tpl="t" /></span></section><section data-mpa-template="t" mpa-from-tpl="t"><section style="box-sizing: border-box;" powered-by="xiumi.us" mpa-from-tpl="t"><section style="margin: 10px 16px;text-align: center;box-sizing: border-box;"><section style="display: inline-block;vertical-align: top;box-sizing: border-box;" mpa-from-tpl="t"><section style="margin-bottom: -6px;line-height: 1em;padding-left: 2px;padding-right: 2px;font-size: 18px;color: rgb(72, 64, 64);box-sizing: border-box;" mpa-from-tpl="t"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;" mpa-from-tpl="t">三、案例实现</strong></p></section><section style="width: 100%;height: 10px;background-color: rgb(255, 202, 0);box-sizing: border-box;" mpa-from-tpl="t"><br /></section></section></section></section></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">假设我们现在有一些房地产项目的成交明细数据,其中有一列为面积,我们在研究中通常需要对分面积段进行研究,那么我们就需要把面积离散化,也就是进行分箱处理。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">就以60-100平内的产品分4个箱为例进行研究。</span><span style="color: rgb(136, 136, 136);font-size: 14px;">(数据样例)</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(136, 136, 136);font-size: 14px;"><br /></span></section><section style="text-align: center;margin-left: 16px;margin-right: 16px;"><img data-backh="703" data-backw="299" data-ratio="2.351170568561873" data-src="https://mmbiz.qpic.cn/mmbiz_png/AJkYaTAujIzzxoemRkynHW2NZwgMMxGH6IpdXW7l6Z2tdDtKGoWoLgaFjdDMalic2IDHHRClhsVE5Lq0uICGEOw/640?wx_fmt=png" data-type="png" data-w="299" style="width: 100%;height: auto;"></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">传统房地产人会通过经验来定一些整数分段</span></strong></span><span style="font-size: 14px;color: rgb(136, 136, 136);">,比如60-80平、80-100平、100-120平这样,当然这也不是不行,我们就以10平米来分段,60-100之间被分成了4段。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><table><tbody><tr><td><section style="margin-left: 16px;margin-right: 16px;">经验性分箱</section></td><td><section style="margin-left: 16px;margin-right: 16px;">方差</section></td><td><section style="margin-left: 16px;margin-right: 16px;">占比</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[60-70)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">0.19</section></td><td><section style="margin-left: 16px;margin-right: 16px;">7%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[70-80)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">17.26</section></td><td><section style="margin-left: 16px;margin-right: 16px;">11%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[80-90)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">17.81</section></td><td><section style="margin-left: 16px;margin-right: 16px;">34%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[90-100)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">14.89</section></td><td><section style="margin-left: 16px;margin-right: 16px;">47%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">总方差</section></td><td><section style="margin-left: 16px;margin-right: 16px;">15.07</section></td><td><br /></td></tr></tbody></table><section style="text-align: left;margin-left: 16px;margin-right: 16px;"><br /></section><section style="text-align: left;margin-left: 16px;margin-right: 16px;"><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">可以看到此时总方差来到了15.07。</span></strong></span></section><section style="text-align: left;margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">那么我们用W-K分箱(加权kmeans,在kmeans分箱的基础上附上套数作为权重),就可以得到结果如下</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;"><br /></span></section><table><tbody><tr><td><section style="margin-left: 16px;margin-right: 16px;">W-K分箱</section></td><td><section style="margin-left: 16px;margin-right: 16px;">方差</section></td><td><section style="margin-left: 16px;margin-right: 16px;">占比</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[69-72)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">0.67</section></td><td><section style="margin-left: 16px;margin-right: 16px;">12%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[79-82)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">0.67</section></td><td><section style="margin-left: 16px;margin-right: 16px;">22%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[89-92)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">0.81</section></td><td><section style="margin-left: 16px;margin-right: 16px;">45%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">[98-100)</section></td><td><section style="margin-left: 16px;margin-right: 16px;">0.23</section></td><td><section style="margin-left: 16px;margin-right: 16px;">21%</section></td></tr><tr><td><section style="margin-left: 16px;margin-right: 16px;">总方差</section></td><td><section style="margin-left: 16px;margin-right: 16px;">0.65</section></td><td><br /></td></tr></tbody></table><section style="margin-left: 16px;margin-right: 16px;"><br /><span style="color: rgb(0, 0, 0);"><strong><span style="font-size: 14px;">此时总方差仅有0.65,分箱效果碾压。</span></strong></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">最后结果也就是89-91平是一个主力面积段,如果数据的分布改变了,分箱的间断点也会随之改变。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">但经验法却总是会指引你去做90-99的产品,也就是有接近10平米的误差了,当然有的人会说,我经验丰富,总是能拍的很准,那也没问题,只是说如果总想用经验,其实很难每次都能获得正确的结论,就像玩俄罗斯轮盘,看起来这是个很简单的游戏,而且听说很少有人在自己的一生中会输第二次。</span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);"><br /></span></section><section style="margin-left: 16px;margin-right: 16px;"><img data-ratio="0.6831501831501832" data-src="https://mmbiz.qpic.cn/mmbiz_jpg/AJkYaTAujIzzxoemRkynHW2NZwgMMxGHevMAlcqyWO9P8s561tgiaXWz56B1oxfuyNpXowqlwwSLgfzww6re3UA/640?wx_fmt=jpeg" data-type="jpeg" data-w="546"></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">更何况算法分箱在无形之中降低了房地产的经验门槛,而且也更加合理。</span></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(136, 136, 136);">最后,在后台发送:K-means分箱,可获取代码。</span></section><section style="margin-left: 16px;margin-right: 16px;"><br /></section><section style="margin-left: 16px;margin-right: 16px;"><span style="font-size: 14px;color: rgb(0, 0, 0);"></span></section><section style="margin-left: 16px;margin-right: 16px;"><span style="background-color: rgb(255, 255, 255);color: rgb(123, 127, 131);font-family: -apple-system, BlinkMacSystemFont, &quot;Helvetica Neue&quot;, &quot;PingFang SC&quot;, &quot;Hiragino Sans GB&quot;, &quot;Microsoft YaHei UI&quot;, &quot;Microsoft YaHei&quot;, Arial, sans-serif;font-size: 12px;letter-spacing: 0.544px;text-align: 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