Impostare il tuo workflow

Vuoi verificare se JobRole da solo può essere un predittore per Attrition. Dato che JobRole è un fattore, pensi di usare un Bayes encoder per rappresentarlo numericamente nel tuo modello.

Il pacchetto embed e i corrispondenti split test e train del dataset attrition sono già caricati nel tuo workspace.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Crea una recipe usando il Bayes encoder.
Assembla il modello e la recipe in un workflow.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

lr_model <- logistic_reg()
# Create recipe using the Bayes encoder
lr_recipe_glm <- 
  recipe(Attrition ~ JobRole, data = train) %>%
  ___(JobRole, outcome = ___(Attrition))

# Bundle with workflow
lr_workflow_glm <-
  workflow() %>%
  add_model(lr_model) %>%
  add_recipe(___)

lr_workflow_glm

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Feature Engineering in R

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Raw data does not always come in its best shape for analysis. In this opening chapter, you will get a first look at how to transform and create features that enhance your model's performance and interpretability.

Exercise 1: What is feature engineering?Exercise 2: A tentative model Exercise 3: Manually engineering a feature Exercise 4: Creating new features using domain knowledge Exercise 5: Setting up your data for analysis Exercise 6: Building a workflow Exercise 7: Increasing the information content of raw data Exercise 8: Identifying missing values Exercise 9: Imputing missing values and creating dummy variables Exercise 10: Fitting and assessing the model Exercise 11: Predicting hotel bookings

In this chapter, you’ll learn that, beyond manually transforming features, you can leverage tools from the tidyverse to engineer new variables programmatically. You’ll explore how this approach improves your models' reproducibility and is especially useful when handling datasets with many features.

Exercise 1: Why transform existing features?Exercise 2: Glancing at your data Exercise 3: Normalizing and log-transforming Exercise 4: Fit and augment Exercise 5: Customize your model assessment Exercise 6: Common feature transformations Exercise 7: Common transformations Exercise 8: Plain recipe Exercise 9: Box-Cox transformation Exercise 10: Yeo-Johnson transformation Exercise 11: Advanced transformations Exercise 12: Baseline Exercise 13: step_poly()Exercise 14: step_percentile()Exercise 15: Who's staying?

You’ll now learn how models often benefit from reducing dimensionality and extracting features from high-dimensional data, including converting text data into numeric values, encoding categorical data, and ranking the predictive power of variables. You’ll explore methods including principal component analysis, kernel principal component analysis, numerical extraction from text, categorical encodings, and variable importance scores.

Exercise 1: Ridurre la dimensionalità Exercise 2: Preparare il terreno Exercise 3: Esplorare la struttura Exercise 4: Percentuale di varianza spiegata Exercise 5: Visualizzare la varianza spiegata Exercise 6: Feature hashing Exercise 7: Esplorare il campo Education Exercise 8: Dentro la matrice Exercise 9: Esplorare l'hashing Exercise 10: Visualizzare l'hashing Exercise 11: Codificare i dati categorici usando l'apprendimento supervisionato Exercise 12: Impostare il tuo workflow

Esercizio in corso

Exercise 13: Adattare, aumentare e valutare Exercise 14: Unire i modelli Exercise 15: Importanza delle variabili Exercise 16: Crea un workflow Exercise 17: Adatta e amplia Exercise 18: Qual è il principale predittore?

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!