Menyiapkan data Anda untuk analisis

Anda akan melihat versi himpunan data nycflights13, dimuat sebagai flights. Himpunan data ini berisi informasi tentang penerbangan yang berangkat dari New York City. Anda ingin memprediksi apakah penerbangan akan tiba terlambat di tujuan, tetapi pertama-tama, Anda perlu menyiapkan data untuk analisis.

Setelah mendiskusikan tujuan model dengan tim ahli, Anda memilih variabel berikut untuk model: flight, sched_dep_time, dep_delay, sched_arr_time, carrier, origin, dest, distance, date, arrival.

Anda juga akan melakukan mutate() pada tanggal menggunakan as.Date() dan mengonversi variabel bertipe character menjadi faktor.

Terakhir, Anda akan membagi data menjadi himpunan data train dan test.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Ubah semua variabel bertipe character menjadi faktor.
Bagi data flights menjadi himpunan test dan train.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

flights <- flights %>%
  select(flight, sched_dep_time, dep_delay, sched_arr_time, carrier, origin, dest, distance, date, arrival) %>%

# Tranform all character-type variables to factors
  mutate(date = as.Date(date), ___(where(is.character), as.factor))

# Split the flights data into test and train sets
set.seed(246)
split <- flights %>% initial_split(prop = 3/4, strata = arrival)
test <- ___(split)
train <- ___(split)

test %>% select(arrival) %>% table() %>% prop.table()
train %>% select(arrival) %>% table() %>% prop.table()

Edit dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

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You’ll wrap up the course by learning about feature engineering and machine learning techniques. You’ll begin by focusing on the problems associated with using all available features in a model and the importance of identifying irrelevant and redundant features and learning to remove these features using embedded methods such as lasso and elastic-net. Next, you’ll explore shrinkage methods such as lasso, ridge, and elastic-net, which can be used to regularize feature weights or select features by setting coefficients to zero. Finally, you’ll finish by focusing on creating an end-to-end feature engineering workflow and reviewing and practicing the previously learned concepts and functions in a small project.

Exercise 1: Reducing the model's features Exercise 2: Sifting through variable importance Exercise 3: Assessing model performance using all available predictors Exercise 4: Building a reduced model Exercise 5: Shrinkage methods Exercise 6: Manual regularization with Lasso Exercise 7: Tuning the penalty Exercise 8: Finalizing the model Exercise 9: Putting it all together Exercise 10: Prep and split Exercise 11: Preprocess Exercise 12: Model Exercise 13: Assess Exercise 14: Congratulations!