Matlab 中面板数据回归比较

发布于 2025-01-19 02:14:39 字数 525 浏览 4 评论 0原文

我有一个非常大的面板数据，想在MATLAB（逻辑回归，决策树，包装的树）中应用许多简单的机器学习技术。

在准备过程中，我遇到了FitGLM和Fitlifetimepdmodel，后者旨在捕获面板数据。我试图了解与FITGLM有何不同，因为当我尝试下面时，结果完全相同。

这是为什么？例如，在FITGLM下，我并不是告诉该程序每个客户都可以拥有多个数据点。

load RetailCreditPanelData.mat

pdModel_1 = fitLifetimePDModel(data,"Logistic", 'AgeVar','YOB', 'IDVar','ID', 'LoanVars','ScoreGroup','ResponseVar','Default');
disp(pdModel_1.Model)

pdModel_2 = fitglm(data,'Default ~ 1 + ScoreGroup + YOB', 'Distribution','binomial', 'link', 'logit');
disp(pdModel_2)

原文

I have a very large panel data and would like to apply a number of simple machine learning techniques in Matlab (Logistic Regression, Decision Trees, Bagged Trees).

During my preparation I came across fitglm and fitLifetimePDModel, the latter of which is meant to capture panel data. I was trying to understand how/if that differs from fitglm because when I try the below, the results are exactly the same.

Why is that? For example, under fitglm I'm not telling the program that each customer can have more than one data points.

load RetailCreditPanelData.mat

pdModel_1 = fitLifetimePDModel(data,"Logistic", 'AgeVar','YOB', 'IDVar','ID', 'LoanVars','ScoreGroup','ResponseVar','Default');
disp(pdModel_1.Model)

pdModel_2 = fitglm(data,'Default ~ 1 + ScoreGroup + YOB', 'Distribution','binomial', 'link', 'logit');
disp(pdModel_2)

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