Sesuaikan penilaian model Anda

Membuat fungsi penilaian kustom sangat praktis saat Anda melakukan iterasi berbagai model. Fungsi metric_set() dari paket yardstick dapat membantu Anda melakukannya.

Definisikan sebuah fungsi yang mengembalikan roc_auc, accuracy, sens (sensitivitas), dan spec (spesifisitas), lalu gunakan fungsi tersebut untuk menilai model Anda.

Kerangka data yang telah ditambah lr_aug sudah dimuat dan siap digunakan.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Definisikan fungsi penilaian kustom yang mengembalikan roc_auc, accuracy, sens, dan spec.
Nilailah model Anda menggunakan fungsi baru tersebut pada lr_aug untuk memperoleh metrik yang baru saja Anda pilih.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Define a custom assessment function 
class_evaluate <- ___(___, ___, sens, ___)

# Assess your model using your new function
___(___, truth = Attrition,
               estimate = .pred_class,
               .pred_No)

Edit dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

4.8+

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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!