如何在R中为网络分类/预测准备变量？

# 使用均值填充缺失值
data$feature <- ifelse(is.na(data$feature), mean(data$feature, na.rm = TRUE), data$feature)

# 使用随机森林进行特征选择
library(randomForest)
rf_model <- randomForest(target ~ ., data = train_data)
importance <- importance(rf_model)
selected_features <- rownames(importance)[importance > threshold]

# 创建新的交互特征
data$new_feature <- data$feature1 * data$feature2

# 加载必要的库
library(dplyr)
library(randomForest)

# 假设我们有一个数据框data
# data <- read.csv("your_data.csv")

# 处理缺失值
data <- data %>%
  mutate(across(where(is.numeric), ~ ifelse(is.na(.), mean(., na.rm = TRUE), .)))

# 特征选择
rf_model <- randomForest(target ~ ., data = train_data)
importance <- importance(rf_model)
selected_features <- rownames(importance)[importance > threshold]

# 特征工程
data$new_feature <- data$feature1 * data$feature2

# 使用选定的特征进行模型训练
model <- train(target ~ ., data = data[, c(selected_features, "target")], method = "rf")