Featuretools

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.

The system functions as a distributed feature synthesis engine, allowing the process of creating feature vectors to scale across multiple cores or clusters to handle large-scale datasets.

The library supports the synthesis of multi-table datasets, time series feature generation, and the creation of custom machine learning primitives when standard automated options are insufficient.

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.

alteryxfeaturetools

Features

Star history