exp(confint(Muti_uni)) ：glm.fit: fitted probabilities numerically 0 or 1 occurred

This error message is indicating that the logistic regression model is having difficulty fitting the data, resulting in probabilities that are either 0 or 1. This can occur when the model is overfitting the data, which means that it is too complex and is fitting the noise in the data rather than the underlying patterns.

One solution is to simplify the model by reducing the number of predictor variables, or by using regularization techniques such as ridge or lasso regression. It may also be helpful to examine the data and ensure that there are no outliers or other issues that could be causing the model to struggle.

相关问题

forest = RandomForestClassifier(max_depth=6, min_samples_leaf=10, n_estimators=1000) def muti_score(model): # warnings.filterwarnings('ignore') accuracy = cross_val_score(model, X_train, Y_train, scoring='accuracy', cv=5) precision = cross_val_score(model, X_train, Y_train, scoring='precision', cv=5) recall = cross_val_score(model, X_train, Y_train, scoring='recall', cv=5) f1_score = cross_val_score(model, X_train, Y_train, scoring='f1', cv=5) auc = cross_val_score(model, X_train, Y_train, scoring='roc_auc', cv=5) print("准确率:",accuracy.mean()) print("精确率:",precision.mean()) print("召回率:",recall.mean()) print("F1_score:",f1_score.mean()) print("AUC:",auc.mean()) muti_score(forest)

这是一段Python代码，定义了一个名为forest的随机森林分类器模型，其中max_depth表示树的最大深度为6，min_samples_leaf表示最小叶子节点样本数为10，n_estimators表示模型中树的数量为1000。
另外，代码还定义了一个名为muti_score的函数，该函数的参数为一个模型，用于计算模型的多个评价指标。

逐行翻译以下代码： clc clear Data_test=readtable(“附件2：调查数据”); Data_cell=table2cell(Data_test); LabelStr = Data_test.Properties.VariableDescriptions; [m,n] = size(Data_test); Data_out = zeros(m,n); Data_out(:,1) = 1:1:m; for i = 2:22 DataLabel = Data_cell(:,i); DataLabel_cat = categorical(DataLabel); DataLabel_int = double(DataLabel_cat); Data_out(:,i) = DataLabel_int; end mutiStr = {[a,b,c,d,e,f,g,h],[aa,bb,cc],[aaa,bbb,ccc,ddd],[aaaa,bbbb,cccc,dddd],[o,p,q,r,s,t],[oo,pp,qq],[ooo,ppp,qqq,rrr,sss],[w,x,y,z]}; Data_out2 = Data_out; Data_muti = cell(m,length(mutiStr));

该代码的功能是读取名为“附件2：调查数据”的表格文件，将其中的数据转换为数字，并将转换后的数据储存在一个名为“Data_out”的矩阵中。同时，代码还创建了一个名为“mutiStr”的单元数组，该数组包含了8个字符串数组。最后，代码还创建了一个名为“Data_muti”的单元数组，该数组大小为(m,length(mutiStr))，即行数为数据表格的行数，列数为字符串数组的个数。

逐行翻译如下：

clc % 清空命令窗口
clear % 清除工作空间变量
Data_test = readtable("附件2：调查数据"); % 读取名为“附件2：调查数据”的表格文件，将数据存储在Data_test中
Data_cell = table2cell(Data_test); % 将Data_test转换为单元格数组，存储在Data_cell中
LabelStr = Data_test.Properties.VariableDescriptions; % 获取表格的变量描述信息，存储在LabelStr中
[m,n] = size(Data_test); % 获取Data_test的行列数，分别存储在m和n中
Data_out = zeros(m,n); % 创建一个大小为(m,n)的零矩阵Data_out
Data_out(:,1) = 1:1:m; % 将Data_out的第一列赋值为1~m的整数
for i = 2:22 % 循环遍历Data_cell的列数，从第二列到第22列
DataLabel = Data_cell(:,i); % 获取Data_cell的第i列数据，存储在DataLabel中
DataLabel_cat = categorical(DataLabel); % 将DataLabel转换为分类数组，存储在DataLabel_cat中
DataLabel_int = double(DataLabel_cat); % 将DataLabel_cat转换为双精度数值数组，存储在DataLabel_int中
Data_out(:,i) = DataLabel_int; % 将DataLabel_int的数据赋值给Data_out的第i列
end
mutiStr = {[a,b,c,d,e,f,g,h],[aa,bb,cc],[aaa,bbb,ccc,ddd],[aaaa,bbbb,cccc,dddd],[o,p,q,r,s,t],[oo,pp,qq],[ooo,ppp,qqq,rrr,sss],[w,x,y,z]}; % 创建一个大小为1x8的单元数组mutiStr，其中包含8个字符串数组
Data_out2 = Data_out; % 将Data_out赋值给Data_out2
Data_muti = cell(m,length(mutiStr)); % 创建一个大小为(m,length(mutiStr))的单元数组Data_muti