What does alteryx/featuretools do?

Featuretools is an automated feature engineering library and data transformation framework written in Python. It automatically generates machine learning feature vectors from multi-table datasets by applying synthesis patterns to relational and timestamped data.

What are the main features of alteryx/featuretools?

The main features of alteryx/featuretools are: Automated Feature Engineering, Deep Feature Synthesis, Modular Primitives, Custom Primitive Definitions, Python Machine Learning Libraries, Data Integration Frameworks, Relational Transformations, Automated Feature Tables.

Which projects share features with alteryx/featuretools?

Projects with overlapping indexed features include: featuretools/featuretools — Featuretools is a Python data science library and automated feature engineering framework designed to create… feast-dev/feast — Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and… apache/spark — Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation… apache/hadoop — Hadoop is a big data infrastructure suite and distributed data processing framework designed to store and process… blue-yonder/tsfresh — tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw… albumentations-team/albumentations — Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep…

Featuretools - automate feature engineering

Name: alteryx/featuretools
Author: alteryx

Features

Automated Feature Engineering - Provides an automated framework for creating machine learning inputs from raw data to improve model accuracy.
Deep Feature Synthesis - Implements deep feature synthesis to automatically generate feature matrices from relational data.
Modular Primitives - Employs modular primitives as the architectural basis for transforming raw columns into engineered features.
Custom Primitive Definitions - Provides interfaces for defining custom primitives to extend the automated feature synthesis logic.
Python Machine Learning Libraries - Provides a Python-based toolkit for generating predictive feature vectors from raw timestamped data.
Data Integration Frameworks - Provides a framework for extracting and transforming relational data into consolidated tables for predictive modeling.
Relational Transformations - Applies relational transformations and aggregation patterns across multiple related tables to synthesize new features.
Automated Feature Tables - Synthesizes single comprehensive tables containing all engineered features from multiple related dataframes.
Schema-Driven Synthesis - Automatically discovers possible feature combinations by analyzing foreign key relationships within data tables.
Distributed Computing - Utilizes distributed computing frameworks to scale feature synthesis across multiple nodes.
Time-Window Aggregations - Implements time-window aggregations to generate temporal features and prevent data leakage.
Large-Scale Data Computation - Processes large-scale data by distributing the synthesis of feature vectors across clusters.
Large-Scale Feature Transformations - Executes feature engineering and transformations across massive datasets using distributed processing.
ML Feature Stores - Generates a wide range of pre-computed feature vectors for use in machine learning models.
Feature Generation - Automatically creates lag and time-based features from timestamped relational datasets.
Feature Engineering - Library for automated feature engineering.
Data Processing and Analysis - Automated feature engineering framework.
Feature Engineering Tools - Automated feature engineering.
Automated Machine Learning - Framework for automated feature engineering from relational data.
Data Science Tooling - Automated feature engineering framework.
Data Science Tools - Automated feature engineering framework.

Projects sharing features with Featuretools

These projects share indexed features with Featuretools. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

featuretools/featuretools
featuretools/featuretools
7,655View on GitHub
Featuretools is a Python data science library and automated feature engineering framework designed to create predictive features from multiple related datasets. It automates the data preparation and transformation steps required for machine learning models through deep feature synthesis. The library enables the automatic generation of comprehensive feature tables by applying recursive transformations to relational data. It supports the transformation of unstructured text into structured numeric features and allows users to define custom primitives to extend the synthesis process with specific
Python
View on GitHub7,655
feast-dev/feast
feast-dev/feast
6,727View on GitHub
Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Pythonbig-datadata-engineeringdata-quality
View on GitHub6,727
apache/spark
apache/spark
43,467View on GitHub
Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e
Scalabig-datajavajdbc
View on GitHub43,467
apache/hadoop
apache/hadoop
15,567View on GitHub
Hadoop is a big data infrastructure suite and distributed data processing framework designed to store and process massive datasets across clusters of computers. It consists of a distributed storage system for managing large files across multiple nodes and a parallel computing engine for processing data across a distributed cluster. The framework implements a distributed file system to ensure fault tolerance and high throughput, paired with a programming model that processes large datasets in parallel. It manages the underlying hardware and software environment required for distributed big dat
Java
View on GitHub15,567

Compare all 30 related projects

Projects sharing features with Featuretools

These projects share indexed features with Featuretools. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

featuretools/featuretools
featuretools/featuretools
7,655View on GitHub
Featuretools is a Python data science library and automated feature engineering framework designed to create predictive features from multiple related datasets. It automates the data preparation and transformation steps required for machine learning models through deep feature synthesis. The library enables the automatic generation of comprehensive feature tables by applying recursive transformations to relational data. It supports the transformation of unstructured text into structured numeric features and allows users to define custom primitives to extend the synthesis process with specific
Python
View on GitHub7,655
feast-dev/feast
feast-dev/feast
6,727View on GitHub
Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Pythonbig-datadata-engineeringdata-quality
View on GitHub6,727
apache/spark
apache/spark
43,467View on GitHub
Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e
Scalabig-datajavajdbc
View on GitHub43,467
apache/hadoop
apache/hadoop
15,567View on GitHub
Hadoop is a big data infrastructure suite and distributed data processing framework designed to store and process massive datasets across clusters of computers. It consists of a distributed storage system for managing large files across multiple nodes and a parallel computing engine for processing data across a distributed cluster. The framework implements a distributed file system to ensure fault tolerance and high throughput, paired with a programming model that processes large datasets in parallel. It manages the underlying hardware and software environment required for distributed big dat
Java
View on GitHub15,567

Compare all 30 related projects

alteryxfeaturetools

Featuretools

Features

Projects sharing features with Featuretools

featuretools/featuretools

feast-dev/feast

apache/spark

apache/hadoop

Frequently asked questions

Star history

Projects sharing features with Featuretools

featuretools/featuretools

feast-dev/feast

apache/spark

apache/hadoop

Frequently asked questions