Feature Engineering

Feature Engineering Definition

Feature engineering refers to the process of using domain knowledge to select and transform the most relevant variables from raw data when creating a predictive model using machine learning or statistical modeling. The goal of feature engineering and selection is to improve the performance of machine learning (ML) algorithms.

This image shows a breakdown of the feature engineering process, with data being analyzed and insights provided.
FAQs

What is Feature Engineering?

The feature engineering pipeline is the preprocessing steps that transform raw data into features that can be used in machine learning algorithms, such as predictive models. Predictive models consist of an outcome variable and predictor variables, and it is during the feature engineering process that the most useful predictor variables are created and selected for the predictive model. Automated feature engineering has been available in some machine learning software since 2016. Feature engineering in ML consists of four main steps: Feature Creation, Transformations, Feature Extraction, and Feature Selection.

Feature engineering consists of creation, transformation, extraction, and selection of features, also known as variables, that are most conducive to creating an accurate ML algorithm. These processes entail:

  • Feature Creation: Creating features involves identifying the variables that will be most useful in the predictive model. This is a subjective process that requires human intervention and creativity. Existing features are mixed via addition, subtraction, multiplication, and ratio to create new derived features that have greater predictive power.  
  • Transformations: Transformation involves manipulating the predictor variables to improve model performance; e.g. ensuring the model is flexible in the variety of data it can ingest; ensuring variables are on the same scale, making the model easier to understand; improving accuracy; and avoiding computational errors by ensuring all features are within an acceptable range for the model. 
  • Feature Extraction: Feature extraction is the automatic creation of new variables by extracting them from raw data. The purpose of this step is to automatically reduce the volume of data into a more manageable set for modeling. Some feature extraction methods include cluster analysis, text analytics, edge detection algorithms, and principal components analysis.
  • Feature Selection: Feature selection algorithms essentially analyze, judge, and rank various features to determine which features are irrelevant and should be removed, which features are redundant and should be removed, and which features are most useful for the model and should be prioritized.

Steps in Feature Engineering

The art of feature engineering may vary among data scientists, however steps for how to perform feature engineering for most machine learning algorithms include the following:

  • Data Preparation: This preprocessing step involves the manipulation and consolidation of raw data from different sources into a standardized format so that it can be used in a model. Data preparation may entail data augmentation, cleaning, delivery, fusion, ingestion, and/or loading. 
  • Exploratory Analysis: This step is used to identify and summarize the main characteristics in a data set through data analysis and investigation. Data science experts use data visualizations to better understand how best to manipulate data sources, to determine which statistical techniques are most appropriate for data analysis, and for choosing the right features for a model. 
  • Benchmark: Benchmarking is setting a baseline standard for accuracy to which all variables are compared. This is done to reduce the rate of error and improve a model’s predictability. Experimentation, testing and optimizing metrics for benchmarking is performed by data scientists with domain expertise and business users.

Examples of Feature Engineering

Feature engineering determines the success of failure of a predictive model, and determines how comprehensible the model will be to humans. Advanced feature engineering is at the heart of the Titanic Competition, a popular feature engineering example developed by Kaggle Fundamentals, an online community of data scientists and subsidiary of Google LLC. This project challenges competitors to predict which passengers survived the sinking of the Titanic.

Each Kaggle competition provides a training data set to train the predictive model, and a testing data set to work with. The Titanic Competition also provides information about passengers onboard the Titanic. Click here to read more about the feature engineering frameworks put to use in the Titanic Competition.

Does OmniSci Offer a Feature Engineering Solution?

OmniSci Immerse is a browser-based, interactive data visualization client that enables users to visually explore data at the speed of thought. Data exploration is a crucial component in feature engineering. The goal of data exploration for machine learning is to gain insights that will inspire subsequent feature engineering and model-building. Feature engineering facilitates the machine learning process and increases the predictive power of machine learning algorithms by creating features from raw data.