逻辑回归-eval(family $ initialize):y值必须为0< = y< = 1

I am trying to perform logistic regression using R in a dataset provided here : http://archive.ics.uci.edu/ml/machine-learning-databases/00451/It is about breast cancer. This dataset contains a column Classification which contains only 1 (if patient doesn't have cancer) or 2 (if patient has cancer)library(ISLR)dataCancer <- read.csv("~/Desktop/Isep/Machine Leaning/TD/Project_Cancer/dataR2.csv")attach(dataCancer)names(dataCancer)summary(dataCancer)cor(dataCancer[,-11])pairs(dataCancer[,-11])#Step : Split data into training and testing datatraining = (BMI>25)testing = !trainingtraining_data = dataCancer[training,]testing_data = dataCancer[testing,]Classification_testing = Classification[testing]#Step : Fit a logistic regression model using training dataas.factor(dataCancer$Classification)classification_model = glm(Classification ~ ., data = training_data,family = binomial )summary(classification_model)运行我的脚本时，我得到:When running my script I get : > classification_model = glm(Classification ~ ., data = training_data,family = binomial )Error in eval(family$initialize) : y values must be 0 <= y <= 1> summary(classification_model)Error in summary(classification_model) : object 'classification_model' not found . 我在其他帖子中添加了 as.factor(dataCancer $ Classification)，但它没有解决我的问题.如果这是该预测变量的内容，您能否建议我将分类的值设置为0到1之间的方法?感谢您的帮助.I added as.factor(dataCancer$Classification) as seen in other posts but it has not solved my problem.Can you suggest me a way to have a classification's value between 0 and 1 if it is the content of this predictor?Thanks for your help.推荐答案您在脚本中添加了 as.factor(dataCancer $ Classification)，即使数据集 dataCancer ，上面的命令不会将数据集变量分类转换为因子.它只会在控制台上返回一个因子.You added the as.factor(dataCancer$Classification) in the script, but even if the dataset dataCancer is attached, a command like the one above does not transform the dataset variable Classification into a factor. It only returns a factor on the console.由于您要在训练数据集上拟合模型，因此可以指定Since you want to fit the model on the training dataset, you either specifytraining_data$Classification <- as.factor(training_data$Classification)classification_model <- glm(Classification ~ ., data = training_data, family = binomial) 或在 glm 行代码中使用 as.factor 函数classification_model <- glm(as.factor(Classification) ~ ., data = training_data, family = binomial) 这篇关于逻辑回归-eval(family $ initialize):y值必须为0&lt; = y&lt; = 1的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！