Melatih dan menilai model

Sekarang setelah Anda menangani nilai hilang dan membuat variabel dummy, saatnya menilai kinerja model Anda!

Himpunan data attrition, beserta pemisahan test dan train, lr_recipe, serta logistic_model() yang telah Anda deklarasikan sudah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Gabungkan model dan recipe dalam workflow.
Latih workflow pada data train.
Hasilkan data frame teraugmentasi untuk penilaian kinerja.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Bundle model and recipe in workflow
lr_workflow <- ___() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe)

# Fit workflow to the train data
lr_fit <- ___(lr_workflow, data = train)

# Generate an augmented data frame for performance assessment
lr_aug <- lr_fit %>% ___(test)

lr_aug %>% roc_curve(truth = Attrition, .pred_No) %>% autoplot()
bind_rows(lr_aug %>% roc_auc(truth = Attrition, .pred_No),          
          lr_aug %>% accuracy(truth = Attrition, .pred_class))

Edit dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

4.8+

120 reviews

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!