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Logit模型与logistic模型的联系与区别? - 知乎
WEBJun 4, 2015 · 罗伊. (1)二者的根本区别在于广义化线性模型中的联系函数的形式。. logit采用对数形式log (a),logistic形式为log (a/1-a)。. (2)应用上,普通logistic的响应变量是二元的,多元logistic的因变量可为多元。. logit的响应变量可以是多元的。. (3)统计软 …
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WEB知乎,让每一次点击都充满意义 —— 欢迎来到知乎,发现问题背后的世界。
Zhihu.com稀有事件回归? - 知乎
WEBEach command comes with an on-line help file. To read the on-line. help for relogit, click "Help" in the Stata menu bar and select. "Stata Command," which causes the Stata command dialog box to appear. Type the name of the RELOGIT command (such as relogit) in the blank. and then click the "OK" button.
Zhihu.com逻辑回归(Logistic regression)在金融领域有什么应用呢?
WEB事实上在市场风险等领域往往用Beta分布来对LGD进行建模。关于Beta分布与logistic回归的关系也是一个非常有趣的问题。当然,前者也可以通过一种广义的线性模型,即Beta regression来直接对beta distribution的参数进行拟合,而不用通过logistic regression。
Zhihu.com如何优化逻辑回归(logistic regression)? - 知乎
WEB很多人觉得logistic regression太简单,却连解释都不做,只一味看参数。. 看看出来的odds ratio和probability,既能给大众解释又不忘建模初衷。. 感觉有图更清楚,几个不错的中文博客,其他资料不一而足. HuJiangtang: 分类模型的性能评估——以SAS Logistic回归为例 (1
Zhihu.com机器学习中的logistic regression的sigmoid函数如何 - 知乎
WEB接下来非常梗概地讲讲我的理解:. 我们需要从GLM(广义线性模型)的角度理解logistic regression,GLM是一个比较大的集合,logistic regression和linear regression都属于GLM. GLM由两个因素决定:Y|X的分布、link function. Y|X的分布大家都知道是什么意思,不多说了. link function是
Zhihu.com逻辑回归(Logistic Regression)(一) - 知乎
WEBFeb 2, 2023 · 简单来说, 逻辑回归(logistic regression)是一种用于解决二分类(0 or 1)问题的机器学习方法,用于估计某种事物的可能性。. 比如某用户购买某商品的可能性,某病人患有某种疾病的可能性,以及某广告被用户点击的可能性等。. 注意,这里用的是“ …
Zhihu.com如何优化逻辑回归(logistic regression)? - 知乎
WEB1.logistic回归基本过程介绍. 目前风暴统计平台可以非常快速准确的完成logistic回归,只需2步! 选入回归自变量 ; 选择自变量筛选方式; 全部是菜单式操作,完成后,界面直接给出规范三线表结果!还可以随着变量的调整实时更新结果,在数据探索初期,可以节省不
Zhihu.comSVM和logistic回归分别在什么情况下使用? - 知乎
WEB如同上面的朋友所言SVM的Hinge Loss(l1/l2)或者LR的Logistic loss都是loss function罢了,和是否要用kernel或者是否linear以及使用什么regularizer没啥关系。 2. SVM跑的总是比LR慢?LR能跑出来的SVM跑不出来?SGD是万能的? 上面有说到,我们的比较要在Formulation是在可比的情况下。
Zhihu.com对于稀有事件(Rare Event)的逻辑回归 Logistic Regression?
WEB逻辑回归(也称“对数几率回归”)(英语:logistic regression 或logit regression),即逻辑模型(英语:Logit model,也译作“评定模型”、“分类评定模型”)是离散选择法模型之一,属于多重变量分析范畴,是社会学、生物统计学、临床、数量心理学、计量经济学、市场营销等统计实证分析的常用方法。
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