wfsets1

statlearning

tidymodels

wfsets

template

Published

November 30, 2023

Aufgabe

Berechnen Sie die Vorhersagegüte (RMSE) für folgende Lernalgorithmen mittesl tidymodels:

lineares Modell

Modellgleichung: body_mass_g ~ bill_length_mm, data = d_train.

Nutzen Sie minimale Vorverarbeitung im Rahmen zweier Rezepte.

Nutzen Sie ein Workflowset.

Lösung

Setup

library(tidymodels)
library(tictoc)  # Zeitmessung
data(penguins, package = "palmerpenguins")

Daten

d <-
  penguins %>% 
  drop_na()

d_split <- initial_split(d)
d_train <- training(d_split)
d_test <- testing(d_split)

Modelle

Lineares Modell:

mod_lin <- linear_reg()

mod_knn <- nearest_neighbor(mode = "regression",
                                  neighbors = tune())

Rezepte

rec_basic <- recipe(body_mass_g ~ bill_length_mm, data = d_train) %>% 
         step_normalize(all_predictors())

rec_plain <- recipe(body_mass_g ~ bill_length_mm, data = d_train)

Resampling

rsmpls <- vfold_cv(d_train, v = 5)

Workflow Set

wf_set <-
  workflow_set(
    preproc = list(rec_simple = rec_basic,
                   rec_plain = rec_plain),
    models = list(mod_lm = mod_lin)
  )

Fitten

tic()
wf_fit <-
  wf_set %>% 
  workflow_map(resamples = rsmpls)
toc()
wf_fit

Check:

wf_fit %>% pluck("result")

Bester Kandidat

autoplot(wf_fit)

autoplot(wf_fit, select_best = TRUE)

collect_metrics(wf_fit)

rank_results(wf_fit, rank_metric = "rmse") %>% 
  filter(.metric == "rmse")

Last Fit

best_wf <-
  wf_fit %>% 
  extract_workflow("rec_simple_mod_lm")

Finalisieren müssen wir diesen Workflow nicht, da er keine Tuningparameter hatte.

fit_final <-
  best_wf %>% 
  last_fit(d_split)

Modellgüte im Test-Set

collect_metrics(fit_final)

--- exname: wfsets1 extype: num exsolution: r fmt(sol) exshuffle: no expoints: 1 categories: - R - statlearning - tidymodels - wfsets - template date: '2023-11-30' title: wfsets1 execute: output: false --- ```{r global-knitr-options, include=FALSE} knitr::opts_chunk$set(fig.pos = 'H', fig.asp = 0.618, fig.width = 4, fig.cap = "", fig.path = "", cache = TRUE, echo = TRUE, message = FALSE, fig.show = "hold") library(tidyverse) library(exams) ``` # Aufgabe Berechnen Sie die Vorhersagegüte (RMSE) für folgende Lernalgorithmen mittesl `tidymodels`: - lineares Modell Modellgleichung: `body_mass_g ~ bill_length_mm, data = d_train`. Nutzen Sie minimale Vorverarbeitung im Rahmen zweier Rezepte. Nutzen Sie ein *Workflowset.* # Lösung ## Setup ```{r} library(tidymodels) library(tictoc) # Zeitmessung data(penguins, package = "palmerpenguins") ``` ## Daten ```{r} d <- penguins %>% drop_na() ``` ```{r} d_split <- initial_split(d) d_train <- training(d_split) d_test <- testing(d_split) ``` ## Modelle Lineares Modell: ```{r} mod_lin <- linear_reg() mod_knn <- nearest_neighbor(mode = "regression", neighbors = tune()) ``` ## Rezepte ```{r} rec_basic <- recipe(body_mass_g ~ bill_length_mm, data = d_train) %>% step_normalize(all_predictors()) ``` ```{r} rec_plain <- recipe(body_mass_g ~ bill_length_mm, data = d_train) ``` ## Resampling ```{r} rsmpls <- vfold_cv(d_train, v = 5) ``` ## Workflow Set ```{r} wf_set <- workflow_set( preproc = list(rec_simple = rec_basic, rec_plain = rec_plain), models = list(mod_lm = mod_lin) ) ``` ## Fitten ```{r} tic() wf_fit <- wf_set %>% workflow_map(resamples = rsmpls) toc() wf_fit ``` Check: ```{r} wf_fit %>% pluck("result") ``` ## Bester Kandidat ```{r} autoplot(wf_fit) ``` ```{r} autoplot(wf_fit, select_best = TRUE) ``` ```{r} collect_metrics(wf_fit) ``` ```{r} rank_results(wf_fit, rank_metric = "rmse") %>% filter(.metric == "rmse") ``` ## Last Fit ```{r} best_wf <- wf_fit %>% extract_workflow("rec_simple_mod_lm") ``` Finalisieren müssen wir diesen Workflow nicht, da er keine Tuningparameter hatte. ```{r} fit_final <- best_wf %>% last_fit(d_split) ``` ## Modellgüte im Test-Set ```{r} collect_metrics(fit_final) ```