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FeatCopilot

🚀 Next-Generation LLM-Powered Auto Feature Engineering Framework

Automatically generate, select, and explain predictive features using semantic understanding of your data


Benchmark Highlights

  • +7.52% Avg Improvement


    Simple models benchmark across 63 datasets

  • #1 FE Tool


    80% win rate vs autofeat and featuretools

  • 90% AutoML Success


    Improves 9/10 datasets with FLAML and AutoGluon

  • +144% Max Improvement


    triple_interaction_regression dataset

View Full Benchmark Results


Two Modes of Operation

Sub-second feature engineering using rule-based transformations:

from featcopilot import AutoFeatureEngineer

# Fast, deterministic feature engineering
engineer = AutoFeatureEngineer(
    engines=['tabular'],
    max_features=50
)
X_transformed = engineer.fit_transform(X, y)  # <1 second

Best for: Production pipelines, real-time inference, reproducible results

Domain-aware semantic feature generation with any LLM provider:

from featcopilot import AutoFeatureEngineer

# LLM-powered semantic features
engineer = AutoFeatureEngineer(
    engines=['tabular', 'llm'],
    max_features=50
)
X_transformed = engineer.fit_transform(
    X, y,
    column_descriptions={'age': 'Patient age in years'},
    task_description='Predict heart disease risk'
)  # 30-60 seconds

Best for: Exploratory analysis, domain-specific features, maximum accuracy


What is FeatCopilot?

FeatCopilot is a Python library for automated feature engineering powered by large language models. It analyzes column meanings and descriptions to generate domain-aware features, applies intelligent selection to keep only the most predictive ones, and provides human-readable explanations for every feature it creates.

  • Multi-Engine Architecture


    Tabular, time series, relational, and text feature engines in one unified API

  • LLM-Powered Intelligence


    Semantic feature discovery, domain-aware generation, and automatic code synthesis

  • Intelligent Selection


    Statistical testing, importance ranking, and redundancy elimination

  • Sklearn Compatible


    Drop-in replacement for scikit-learn transformers in your ML pipelines

Why FeatCopilot?

Feature FeatCopilot Featuretools TSFresh AutoFeat OpenFE CAAFE
Tabular Features
Time Series ⚠️
Relational
LLM-Powered ⚠️
Semantic Understanding ⚠️
Code Generation ⚠️
Sklearn Compatible
Interpretable ⚠️ ⚠️

Installation

# Basic installation
pip install featcopilot

# With LLM capabilities
pip install featcopilot[llm]

# Full installation with all extras
pip install featcopilot[full]

Getting Started

License

FeatCopilot is released under the MIT License.