https://wiki.statsape.com/index.php?action=history&feed=atom&title=%E7%AD%9B%E6%9F%A5%E8%87%AA%E5%8F%98%E9%87%8F%E5%85%B1%E7%BA%BF%E6%80%A7 2026-06-09T21:24:32Z 本wiki上该页面的版本历史 MediaWiki 1.39.6 https://wiki.statsape.com/index.php?title=%E7%AD%9B%E6%9F%A5%E8%87%AA%E5%8F%98%E9%87%8F%E5%85%B1%E7%BA%BF%E6%80%A7&diff=6135&oldid=prev 2024-01-19T11:25:13Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="zh-Hans-CN"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">←上一版本</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">2024年1月19日 (五) 19:25的版本</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l7">第7行：</td> <td colspan="2" class="diff-lineno">第7行：</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|productionstatedesc=在[[Update:DecisionLinnc 1.0.2.0|V1.0.2]]部署</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|productionstatedesc=在[[Update:DecisionLinnc 1.0.2.0|V1.0.2]]部署</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|nodeenglishname=<del style="font-weight: bold; text-decoration: none;">[[Has english name::</del>Collinearity Independent Variable Filter<del style="font-weight: bold; text-decoration: none;">]]</del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|nodeenglishname=Collinearity Independent Variable Filter</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|abbreviation=<del style="font-weight: bold; text-decoration: none;">[[Has abbreviation::</del>CVIF<del style="font-weight: bold; text-decoration: none;">]]</del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|abbreviation=CVIF</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|funcmaincategory=数据分析</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|funcmaincategory=数据分析</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|funcsubcategory=[[DataAGM Lv1 Cat::多元分析]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|funcsubcategory=[[DataAGM Lv1 Cat::多元分析]]</div></td></tr> <tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l25">第25行：</td> <td colspan="2" class="diff-lineno">第25行：</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|statsapewikiurl=https://wiki.statsape.com/筛查自变量共线性</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|statsapewikiurl=https://wiki.statsape.com/筛查自变量共线性</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|previousnode=[[多重对应分析]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|previousnode=[[多重对应分析]]</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|nextnode=[[<del style="font-weight: bold; text-decoration: none;">导出训练集</del>]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; 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Zeroclanzhang https://wiki.statsape.com/index.php?title=%E7%AD%9B%E6%9F%A5%E8%87%AA%E5%8F%98%E9%87%8F%E5%85%B1%E7%BA%BF%E6%80%A7&diff=5351&oldid=prev 2024-01-18T14:22:16Z

<p>创建页面，内容为“{{Infobox nodebasic |nodename=筛查自变量共线性 |nodeimage=Collinearity Independent Variable Filter.png |icon=Collinearity Independent Variable Filter.svg |simpleicon=Collinearity Independent Variable Filter_Pure.svg |developer=Dev.Team-DPS |productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用 |productionstatedesc=在<a href="/index.php?title=Update:DecisionLinnc_1.0.2.0&amp;action=edit&amp;redlink=1" class="new" title="Update:DecisionLinnc 1.0.2.0（页面不存在）">V1.0.2</a>部署 |nodeenglishname=Has english name::Collinearity Independent Va…”</p> <p><b>新页面</b></p><div>{{Infobox nodebasic <br /> |nodename=筛查自变量共线性<br /> |nodeimage=Collinearity Independent Variable Filter.png<br /> |icon=Collinearity Independent Variable Filter.svg<br /> |simpleicon=Collinearity Independent Variable Filter_Pure.svg<br /> |developer=Dev.Team-DPS<br /> |productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用<br /> |productionstatedesc=在[[Update:DecisionLinnc 1.0.2.0|V1.0.2]]部署<br /> |nodeenglishname=[[Has english name::Collinearity Independent Variable Filter]]<br /> |abbreviation=[[Has abbreviation::CVIF]]<br /> |funcmaincategory=数据分析<br /> |funcsubcategory=[[DataAGM Lv1 Cat::多元分析]]<br /> |nodecategory=数据挖掘<br /> |nodeinterpretor=R<br /> |nodeshortdescription=&lt;p&gt;自变量之间存在多重共线性时，最简单的方法就是对共线的自变量进行一定的筛选，保留更为重要的变量，删除次要或可替代的变量，从而减少变量之间的重复信息，避免在模型拟合时出现多重共线性的问题。生成的表表示经过筛查后不共线的变量。&lt;/p&gt;&lt;p&gt;用途：筛查自变量之间的共线性有助于确保回归模型的有效性、稳定性和可解释性。&lt;/p&gt;&lt;p&gt;参数：选择因变量，和自变量。&lt;/p&gt;<br /> |nodeinputnumber=4<br /> |nodeoutputnumber=3<br /> |nodeloopsupport=是<br /> |nodeifswitchsupport=否<br /> |nodeavailableplotlist=nodenoplotoutput<br /> |nodeavailabletablelist=GVIF;VIF<br /> |nodeconfiguration=VariableList;Text<br /> |nodeinputports=WorkFlow-Control ➤;Transfer-Variable ◆;Transfer-Table ■<br /> |nodeoutputports=WorkFlow-Control ➤;Transfer-Variable ◆;Transfer-Table ■<br /> |statsapewikiurl=https://wiki.statsape.com/筛查自变量共线性<br /> |previousnode=[[多重对应分析]]<br /> |nextnode=[[导出训练集]]<br /> }}<br /> <br /> <br /> {{Navplate AlgorithmNodeList}}<br /> <br /> [[Category:多元分析]]</div>

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