Ön işleme

Feature engineering zamanı! Gözlem kimliği gibi bilgi değeri düşük ama işe yarar olabilecek değişkenlerle ilgilenecek ya da eksik değerleri ele alacak bir recipe oluşturman gerekiyor. Bu aynı zamanda bazı öngörücüleri dönüştürmek için de bir fırsat. Örneğin, sayısal özellikleri normalize edip kategorik olanlar için kukla değişkenler oluşturabilirsin.

attrition veri kümesi ile önceki egzersizde oluşturduğun train ve test ayrımları çalışma ortamında hazır.

Bu egzersiz

R'da Feature Engineering

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Tüm sayısal özellikleri normalize et.
Eksik değerleri knn atama (imputation) algoritması ile tahmin et.
Tüm nominal öngörücüler için kukla değişkenler oluştur.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

recipe <- recipe(Attrition ~ ., data = train) %>%
  update_role(...1, new_role = "ID") %>%

# Normalize all numeric features
  ___(all_numeric_predictors()) %>% 

# Impute missing values using the knn imputation algorithm
  ___(all_predictors()) %>%

# Create dummy variables for all nominal predictors
  ___(all_nominal_predictors())
 
recipe

Kodu Düzenle ve Çalıştır

Bu egzersiz

R'da Feature Engineering

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

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: 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: Modelin özelliklerini azaltma Exercise 2: Değişken önemini süzmek Exercise 3: Tüm mevcut yordayıcıları kullanarak model performansını değerlendirme Exercise 4: Azaltılmış bir model kurma Exercise 5: Daralma (shrinkage) yöntemleri Exercise 6: Lasso ile manuel düzenlileştirme Exercise 7: Ceza değerini ayarlama Exercise 8: Modeli sonlandırma Exercise 9: Hepsini bir araya getirmek Exercise 10: Hazırla ve böl Exercise 11: Ön işleme

Geçerli Egzersiz

Exercise 12: Model Exercise 13: Değerlendir Exercise 14: Tebrikler!