Feature Selection - Nested Cross-Validation
发布时间
阅读量：
阅读量
基于泰坦尼克号数据集构建分类型机器学习模型（用于预测乘客生存状态）
数据预处理：
library(mlr3verse)
library(mlr3fselect)
set.seed(7832)
lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")
library(mlr3data)
data("titanic", package = "mlr3data")
titanic$age[is.na(titanic$age)] = median(titanic$age, na.rm = TRUE)
titanic$embarked[is.na(titanic$embarked)] = "S"
titanic$ticket = NULL
titanic$name = NULL
titanic$cabin = NULL
titanic = titanic[!is.na(titanic$survived),]
构建机器学习相关任务
全部评论 (0)
还没有任何评论哟~