library(tidyverse)
library(tidymodels)

Consigna

En este notebook vamos a realizar tunning de un árbol de decisión como en la práctica guiada 2.

Dataset

El dataset es el mismo del TP1 y se encuentra disponible en:

https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data

Lo podemos ingresar mediante el siguiente comando

train <- read_csv("https://raw.githubusercontent.com/data-datum/datasets/main/train_house.csv")
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   Id = col_double(),
##   MSSubClass = col_double(),
##   LotFrontage = col_double(),
##   LotArea = col_double(),
##   OverallQual = col_double(),
##   OverallCond = col_double(),
##   YearBuilt = col_double(),
##   YearRemodAdd = col_double(),
##   MasVnrArea = col_double(),
##   BsmtFinSF1 = col_double(),
##   BsmtFinSF2 = col_double(),
##   BsmtUnfSF = col_double(),
##   TotalBsmtSF = col_double(),
##   `1stFlrSF` = col_double(),
##   `2ndFlrSF` = col_double(),
##   LowQualFinSF = col_double(),
##   GrLivArea = col_double(),
##   BsmtFullBath = col_double(),
##   BsmtHalfBath = col_double(),
##   FullBath = col_double()
##   # ... with 18 more columns
## )
## See spec(...) for full column specifications.

La variable a predecir es la de SalePrice.