Je workflow opzetten

Je wilt onderzoeken of alleen JobRole een voorspeller kan zijn voor Attrition. Omdat JobRole een factor is, wil je een Bayes-encoder gebruiken om deze numeriek te representeren in je model.

Het embed-pakket en de bijbehorende test- en train-splits uit de attrition-gegevensset zijn geladen in je werkruimte.

Deze oefening maakt deel uit van de cursus

Feature engineering in R

Cursus bekijken

Oefeninstructies

Maak een recipe met de Bayes-encoder.
Bundel het model en de recipe in een workflow.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Feature engineering in R

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Begin de cursus gratis

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: Dimensionaliteit verminderen Exercise 2: Het podium voorbereiden Exercise 3: De structuur induiken Exercise 4: Percentage verklaarde variantie Exercise 5: Variantie visualiseren Exercise 6: Feature hashing Exercise 7: Onderzoeken van education field Exercise 8: De matrix in Exercise 9: De hashing verkennen Exercise 10: De hashing visualiseren Exercise 11: Categorische data coderen met supervised learning Exercise 12: Je workflow opzetten

Huidige oefening

Exercise 13: Fitten, aanvullen en beoordelen Exercise 14: Modellen samenvoegen Exercise 15: Belang van variabelen Exercise 16: Maak een workflow Exercise 17: Fitten en augmentereren Exercise 18: Wat is de belangrijkste voorspeller?

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!