Setacciare l'importanza delle variabili

Il dataset attrition contiene 839 osservazioni e 30 predittori per "Attrition". Vuoi esplorare il compromesso tra le prestazioni di un modello che usa tutti i predittori disponibili e quelle di un modello ridotto basato su poche variabili informative.

In questo esercizio, eseguirai il fit di un modello e osserverai l'importanza delle variabili del modello addestrato. Nell'esercizio successivo, valuterai le prestazioni confrontando questo modello con un modello ridotto.

Gli split train e test e il pacchetto vip() sono disponibili nel tuo ambiente insieme a un modello di regressione logistica model già dichiarato.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Crea una recipe che modelli Attrition usando tutti i predittori.
Esegui il fit del workflow sui dati di training.
Usa l'oggetto fit_full per tracciare l'importanza delle variabili del tuo modello.
Applica la funzione extract_fit_parsnip() prima di vip() per fornirgli le informazioni richieste.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a recipe that models Attrition using all the predictors
recipe_full <- ___(___, data = train)

workflow_full <- workflow() %>%
  add_model(model) %>%
  add_recipe(recipe_full)

# Fit the workflow to the training data
fit_full <- ___ %>%
  ___(data = train)

# Use the fit_full object to graph the variable importance of your model. Apply extract_fit_parsnip() function before vip()
fit_full %>% ___() %>%
  ___(aesthetics = list(fill = "steelblue"))

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: Reducing dimensionality Exercise 2: Prepping the stage Exercise 3: Digging into the structure Exercise 4: Percent of variance explained Exercise 5: Visualizing variance explained Exercise 6: Feature hashing Exercise 7: Investigating education field Exercise 8: Into the matrix Exercise 9: Exploring the hashing Exercise 10: Visualizing the hashing Exercise 11: Encoding categorical data using supervised learning Exercise 12: Setting up your workflow Exercise 13: Fitting, augmenting, and assessing Exercise 14: Binding models together Exercise 15: Variable Importance Exercise 16: Create a workflow Exercise 17: Fit and augment Exercise 18: Which is the main predictor?

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: Ridurre le feature del modello Exercise 2: Setacciare l'importanza delle variabili

Esercizio in corso

Exercise 3: Valutare le prestazioni del modello usando tutti i predittori disponibili Exercise 4: Creare un modello ridotto Exercise 5: Metodi di shrinkage Exercise 6: Regolarizzazione manuale con Lasso Exercise 7: Ottimizzare la penalty Exercise 8: Finalizzazione del modello Exercise 9: Mettere tutto insieme Exercise 10: Prepara e dividi Exercise 11: Preprocess Exercise 12: Modello Exercise 13: Valuta Exercise 14: Congratulazioni!