为什么输出结果预测准确率只有百分之2？

非常抱歉，这是我在代码中的一个小错误。逻辑回归模型的预测准确率应该使用“mean(pred == test$Sex)”来计算，而不是使用“accuracy(pred, test$Sex)”函数。因此，以下是修改后的代码：

# 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
accuracy <- mean(pred == test$Sex)
cat("\n预测准确率:", round(accuracy * 100, 2), "%")

现在您应该能够得到更合理的预测准确率结果了。非常抱歉给您带来不便。