Course
Feature Engineering in R
IntermediateSkill Level
Updated 03/2023RMachine Learning4 hr14 videos58 Exercises4,950 XP2,574Statement of Accomplishment
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Discover Feature Engineering for Machine Learning
In this course, you’ll learn about feature engineering, which is at the heart of many times of machine learning models. As the performance of any model is a direct consequence of the features it’s fed, feature engineering places domain knowledge at the center of the process. You’ll become acquainted with principles of sound feature engineering, helping to reduce the number of variables where possible, making learning algorithms run faster, improving interpretability, and preventing overfitting.Implement Feature Engineering Techniques in R
You will learn how to implement feature engineering techniques using the R tidymodels framework, emphasizing the recipe package that will allow you to create, extract, transform, and select the best features for your model.Engineer Features and Build Better ML Models
When faced with a new dataset, you will be able to identify and select relevant features and disregard non-informative ones to make your model run faster without sacrificing accuracy. You will also become comfortable applying transformations and creating new features to make your models more efficient, interpretable, and accurate!Prerequisites
Supervised Learning in R: ClassificationSupervised Learning in R: Regression1
Introducing Feature Engineering
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.
2
Transforming Features
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.
3
Extracting Features
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.
4
Selecting Features
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.
Feature Engineering in R
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FAQs
What R framework does this course use for feature engineering?
You use the tidymodels framework, with emphasis on the recipes package, to create, transform, extract, and select features as part of a reproducible modeling workflow.
What feature selection methods are taught in this course?
You learn embedded methods like lasso and elastic-net that regularize feature weights or set coefficients to zero, along with techniques to identify irrelevant and redundant features.
Is this suitable for someone who has only taken introductory R courses?
No. This is an advanced course with nine prerequisites spanning regression, classification, statistics, and data visualization. Solid R and supervised learning experience is expected.
Does the course cover principal component analysis?
Yes. Chapter 3 covers PCA and kernel PCA for extracting features from high-dimensional data, along with text extraction, categorical encodings, and variable importance scores.
Will I build a complete feature engineering workflow by the end?
Yes. The final chapter guides you through an end-to-end feature engineering project that combines all the concepts and functions learned throughout the course.
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