Customize your model assessment

Creating custom assessment functions is quite convenient when iterating through various models. The metric_set() function from the yardstickpackage can help you to achieve this.

Define a function that returns roc_auc, accuracy, sens(sensitivity) and specificity spec(specificity) and use it to assess your model.

The augmented data frame lr_augis already loaded and ready to go.

Este exercício faz parte do curso

Feature Engineering in R

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Instruções do exercício

Define a custom assessment function that returns roc_auc, accuracy, sens, and spec.
Assess your model using your new function on lr_augto obtain the metrics you just chose.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

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

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

Editar e executar o código

Feature Engineering in R

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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!