Feature Extraction and Engineering

Feature Extraction and Engineering

Feature extraction and engineering is the iterative process of creating the features and labels needed to train the model through a series of data transformations. It is often performed in lockstep with model training, because the ability to identify helpful features can have a significant impact on the overall success of the modeling effort. Simple examples of feature engineering include generating derived features (such as calculating an age from a birthdate) or converting categorical variables (such as transaction types) into one-hot encoded, or binary, vectors (e.g. turning “male/female” data into male=o, female=1.)

With deep learning, features are usually straightforward because deep neural networks (DNNs) generate their own internal transformations. With traditional machine learning, feature engineering can be quite challenging and relies heavily on the creativity and experience of the data scientist and their understanding of the business domain or ability to effectively collaborate with domain experts.

SAS Visual Data Mining and Machine Learning
Solve the most complex analytical problems with a single, integrated, collaborative solution
Take control of your R code
RapidMiner Studio
One platform, does everything
An enterprise-grade platform for agile, reproducible, and scalable machine learning
The enterprise feature store
Machine learning made beautifully simple for everyone
Oracle – Data Science Platform
Data science platform
Creating data science
IBM Watson Studio
Build and scale trusted AI on any cloud. Automate the AI lifecycle for ModelOps
Google Vertex AI
Fully managed, end-to-end platform for data science and machine learning