Preprocessing and Feature Engineering Framework for Machine Learning Models

An extensible framework for pipeable sequences of feature engineering steps, created by Max Kühn. The framework provides preprocessing tools where statistical parameters can be estimated from an initial dataset and applied to others. The processed output is designed for use as input to statistical or machine learning models.

Use Cases

• Creating reproducible data preprocessing pipelines based on the described pipeable framework.
• Estimating and applying statistical transformation parameters across datasets based on the described method.
• Preparing tabular data for machine learning models using the provided feature engineering steps.

Strengths

• Framework is described as extensible, allowing for customization.
• Supports parameter estimation from one dataset for application to others, promoting consistency.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Max Kühn via paperswithcode

Collection Method

Framework publication.

Time Range

Temporal coverage is unknown.

Freshness

Last updated date is unknown.

Geography

Spatial coverage is unknown.