请把你的代码单独发给我
时间: 2024-03-25 10:39:06 浏览: 14
好的,以下是修改后适用于逻辑回归模型的代码:
```R
# load the data
abalone <- read.csv("abalone.data", header = FALSE)
# set column names
colnames(abalone) <- c("Sex", "Length", "Diameter", "Height", "Whole_weight", "Shucked_weight", "Viscera_weight", "Shell_weight", "Rings")
# convert "Sex" variable to a factor
abalone$Sex <- as.factor(ifelse(abalone$Sex == "M", "M", "F"))
# split the data into training and testing sets
set.seed(123) # for reproducibility
trainIndex <- sample(1:nrow(abalone), 0.7*nrow(abalone))
train <- abalone[trainIndex,]
test <- abalone[-trainIndex,]
# build a logistic regression model
model <- glm(Sex ~ Length + Diameter + Height + Whole_weight + Shucked_weight + Viscera_weight + Shell_weight + Rings, data = train, family = binomial(link = 'logit'))
# make predictions on the testing set
pred <- predict(model, newdata = test, type = 'response')
pred <- ifelse(pred > 0.5, 'M', 'F')
# evaluate the performance of the model
library(Metrics)
accuracy <- accuracy(pred, test$Sex)
cat("\n预测准确率:", round(accuracy, 2), "%")
```
请注意,逻辑回归模型的预测结果是二元分类,所以我们的目标变量从“Rings”变成了“Sex”。另外,我们需要使用“binomial”家族的函数来告诉R我们正在运用逻辑回归模型进行二元分类。