Tools for building scorecard models in python, with a sklearn-compatible API
Project description
skorecard
skorecard
is a scikit-learn compatible python package that helps streamline the development of credit risk acceptance models (scorecards).
Scorecards are ‘traditional’ models used by banks in the credit decision process. Internally, scorecards are Logistic Regressions models that make use of features that are binned into different groups. The process of binning is usually done manually by experts, and skorecard
provides tools to makes this process easier. skorecard
is built on top of scikit-learn as well as other excellent open source projects like optbinning, dash and plotly.
Features ⭐
- Automate bucketing of features inside scikit-learn pipelines.
- Dash webapp to help manually tweak bucketing of features with business knowledge (not yet available)
- Extension to
sklearn.linear_model.LogisticRegression
that is also able to report p-values - Plots and reports to speed up analysis and writing technical documentation.
Installation
pip3 install skorecard
Documentation
See ing-bank.github.io/skorecard/.
Presentations
Title | Host | Date | Speaker(s) |
---|---|---|---|
Skorecard: Making logistic regressions great again | ING Data Science Meetup | 10 June 2021 | Daniel Timbrell, Sandro Bjelogrlic, Tim Vink |
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