Fitting, augmenting, and assessing

You are ready to fit the workflow to the training data and assess its performance using the testing split.

The embed package, the lr_workflow_glm object, and the corresponding test and train splits are in your workspace.

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Feature Engineering in R

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คำแนะนำการฝึกหัด

Fit the workflow to the training set.
Augment the fit using the test split.
Assess the model.

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ

ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์

# Fit the workflow to the training set
lr_fit_glm <- lr_workflow_glm %>%
  ___(train)

# Augment the fit using the test split
lr_aug_glm <- lr_fit_glm %>%
  ___(test)

# Assess the model
glm_metrics <- lr_aug_glm %>%
  class_evaluate(truth = ___,
                 estimate = .pred_class,
                 .pred_Yes)

glm_metrics

แก้ไขและรันโค้ด

Feature Engineering in 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!