Dentro la matrice

Hai identificato sei valori distinti per EducationField. Ma sospetti che possano comparirne altri quando esegui il modello su nuovi dati. Per prepararti, creerai un indice di hashing con 50 termini. Il pacchetto textrecipes e le partizioni attrition_train e attrition_test sono già caricate.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Aggiungi un passaggio alla recipe che generi un indice dummy_hash per EducationField.
Prepara la recipe.
Esegui il bake della recipe preparata.
Unisci la tabella ottenuta dal bake e i valori di EducationField in un'unica tabella e stampa le prime 7 righe, oltre alle colonne 1 e dalla 18 alla 20.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

recipe <- recipe(~EducationField, data = attrition_train) %>%
# Add a step to the recipe that generates a dummy_hash index for EducationField
  ___(EducationField, prefix = NULL, signed = FALSE, num_terms = 50L)

# Prepare the recipe
object <- recipe %>%
  ___

# Bake the prepped recipe
baked <- ___(object, new_data = attrition_test)

# Bind the baked recipe table and the EducationField values into one table
bind_cols(___, baked)[1:7,c(1,18:20)]

Modifica ed esegui il codice

Feature Engineering in R

IntermediárioNível de habilidade

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!