使用LightGBM进行电商评论有用性识别的R语言代码是什么

以下是使用LightGBM进行电商评论有用性识别的R语言代码示例：

# 加载必要的库
library(data.table)
library(lightgbm)

# 读入数据
train <- fread("train.csv")
test <- fread("test.csv")

# 划分训练集和验证集
set.seed(123)
idx <- sample(1:nrow(train), size = 0.8*nrow(train))
train_data <- train[idx, ]
valid_data <- train[-idx, ]

# 定义特征和目标变量
features <- c("review_length", "review_rating", "review_count", "reviewer_rating")
target <- "useful"

# 转换数据格式
train_data <- lgb.Dataset(train_data[, features], label = train_data[[target]])
valid_data <- lgb.Dataset(valid_data[, features], label = valid_data[[target]])

# 定义模型参数
params <- list(objective = "binary", metric = "auc", boosting_type = "gbdt",
num_leaves = 31, learning_rate = 0.05, feature_fraction = 0.9)

# 训练模型
model <- lgb.train(params, train_data, valid_sets = list(train_data, valid_data),
num_boost_round = 1000, early_stopping_rounds = 50)

# 在测试集上预测
pred <- predict(model, test[, features])

# 保存结果
result <- data.frame(review_id = test$review_id, useful = pred)
write.csv(result, "result.csv", row.names = FALSE)