rf-usemodels

tidymodels

statlearning

template

string

Published

May 17, 2023

Aufgabe

Berechnen Sie ein prädiktives Modell mit dieser Modellgleichung:

body_mass_g ~ . (Datensatz: palmerpenguins::penguins).

Nutzen Sie usemodels!

Hinweise: - Tunen Sie mtry - Verwenden Sie Kreuzvalidierung - Verwenden Sie Standardwerte, wo nicht anders angegeben. - Fixieren Sie Zufallszahlen auf den Startwert 42.

Lösung

Vorbereitung:

# Setup:
library(tidymodels)

── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──

✔ broom        1.0.5     ✔ recipes      1.0.8
✔ dials        1.2.0     ✔ rsample      1.2.0
✔ dplyr        1.1.3     ✔ tibble       3.2.1
✔ ggplot2      3.4.4     ✔ tidyr        1.3.0
✔ infer        1.0.5     ✔ tune         1.1.2
✔ modeldata    1.2.0     ✔ workflows    1.1.3
✔ parsnip      1.1.1     ✔ workflowsets 1.0.1
✔ purrr        1.0.2     ✔ yardstick    1.2.0

── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
✖ purrr::discard() masks scales::discard()
✖ dplyr::filter()  masks stats::filter()
✖ dplyr::lag()     masks stats::lag()
✖ recipes::step()  masks stats::step()
• Use suppressPackageStartupMessages() to eliminate package startup messages

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ readr     2.1.4
✔ lubridate 1.9.3     ✔ stringr   1.5.0

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ readr::col_factor() masks scales::col_factor()
✖ purrr::discard()    masks scales::discard()
✖ dplyr::filter()     masks stats::filter()
✖ stringr::fixed()    masks recipes::fixed()
✖ dplyr::lag()        masks stats::lag()
✖ readr::spec()       masks yardstick::spec()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tictoc)  # Zeitmessung


# Data:
d_path <- "https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv"
d <- read_csv(d_path)

Rows: 344 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): species, island, sex
dbl (6): rownames, bill_length_mm, bill_depth_mm, flipper_length_mm, body_ma...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# rm NA in the dependent variable:
d <- d %>% 
  drop_na(body_mass_g)


set.seed(42)
d_split <- initial_split(d)
d_train <- training(d_split)
d_test <- testing(d_split)

library(usemodels)
use_ranger(body_mass_g ~ ., data = d_train)

ranger_recipe <- 
  recipe(formula = body_mass_g ~ ., data = d_train) 

ranger_spec <- 
  rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>% 
  set_mode("classification") %>% 
  set_engine("ranger") 

ranger_workflow <- 
  workflow() %>% 
  add_recipe(ranger_recipe) %>% 
  add_model(ranger_spec) 

set.seed(32162)
ranger_tune <-
  tune_grid(ranger_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))

Oder die resultierende Syntax in die Zwischenablage kopieren lassen:

use_ranger(body_mass_g ~ ., data = d_train,
           clipboard = TRUE)  # kopiert Syntax in die Zwischenablage

Categories:

tidymodels
statlearning
template
string