Featuretools automatically creates features from
temporal and relational datasets
Deep Feature Synthesis
Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling.
Precise Handling of Time
Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. You can specify prediction times row-by-row.
Reusable Feature Primitives
Featuretools comes with a library of low-level functions which can be stacked to create features. You can build and share your own custom primitives to be reused on any dataset.
Why use Featuretools?
Improve your existing workflow
Featuretools works alongside tools you already use to build machine learning pipelines. You can load in pandas dataframes and automatically create meaningful features in a fraction of the time it would take to do manually.
Accessible Python API
With several demo applications, extensive documentation and community support on Stack Overflow, getting started with Featuretools is easier than ever. Take a look at the Demos page to get started.
Alteryx Open Source Tools
Automated Machine Learning
EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines.
Prediction Engineering
Compose is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning.
Want to run featuretools on bigger datasets faster?
— Featuretools (@featuretools_py) June 22, 2020
Featuretools supports parallelizing and distributing feature engineering computation using Dask Dataframes 🔥. Simply replace pandas with @dask_dev to get going
Learn more: https://t.co/Hjl3XcI9ez pic.twitter.com/ybBUIvq66l
Featuretools v0.17.0 is out 🎉
— Featuretools (@featuretools_py) July 8, 2020
You can now visualize feature definitions by running featuretools.graph_feature().
This makes it easier to audit the calculations and which columns from each table are being used to create the final feature values.
Happy Feature Engineering! pic.twitter.com/oanpr5qn9V
Wow, it's been 1 year since we open sourced @featuretools. I'm so proud of the @feature_labs team and the Featuretools community who have helped it mature into the most popular library for automated feature engineering. From one year ago: https://t.co/KHVL6rIICg.
— Max Kanter (@maxk) September 27, 2018
