Workflow'unu kurma

JobRole tek başına Attrition için bir kestirici olabilir mi diye incelemek istiyorsun. JobRole bir faktör olduğundan, modelinde bunu sayısal olarak temsil etmek için bir Bayes kodlayıcı kullanmayı planlıyorsun.

embed paketi ve attrition veri kümesinden gelen ilgili test ve train bölmeleri çalışma alanına yüklendi.

Bu egzersiz

R'da Feature Engineering

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Bayes kodlayıcıyı kullanarak bir recipe oluştur.
Model ve recipe'i bir workflow içinde bir araya getir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

R'da Feature Engineering

kursunun bir parçasıdır

IntermediárioNível de habilidade

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Kursa Ücretsiz Başlayın

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: Boyut indirgeme Exercise 2: Sahneyi hazırlamak Exercise 3: Yapının derinlerine inmek Exercise 4: Açıklanan varyans yüzdesi Exercise 5: Açıklanan varyansı görselleştirme Exercise 6: Özellik karması Exercise 7: Eğitim alanını inceleme Exercise 8: Matrise doğru Exercise 9: Hashing'i keşfetme Exercise 10: Hashing’i görselleştirme Exercise 11: Denetimli öğrenme kullanarak kategorik verileri kodlama Exercise 12: Workflow'unu kurma

Geçerli Egzersiz

Exercise 13: Uydurma, zenginleştirme ve değerlendirme Exercise 14: Modelleri birleştirme Exercise 15: Değişken Önemi Exercise 16: Bir iş akışı oluştur Exercise 17: Uydur ve zenginleştir Exercise 18: Ana yordayıcı hangisi?

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!