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 Regression 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.
:point_right: Read the blogpost introducing skorecard
Features ⭐
- Automate bucketing of features inside scikit-learn pipelines.
- Dash webapp to help manually tweak bucketing of features with business knowledge
- Extension to
sklearn.linear_model.LogisticRegressionthat is also able to report p-values - Plots and reports to speed up analysis and writing technical documentation.
Quick demo
skorecard offers a range of bucketers:
import pandas as pd
from skorecard.bucketers import EqualWidthBucketer
df = pd.DataFrame({'column' : range(100)})
ewb = EqualWidthBucketer(n_bins=5)
ewb.fit_transform(df)
ewb.bucket_table('column')
#> bucket label Count Count (%)
#> 0 -1 Missing 0.0 0.0
#> 1 0 (-inf, 19.8] 20.0 20.0
#> 2 1 (19.8, 39.6] 20.0 20.0
#> 3 2 (39.6, 59.400000000000006] 20.0 20.0
#> 4 3 (59.400000000000006, 79.2] 20.0 20.0
#> 5 4 (79.2, inf] 20.0 20.0
That also support a dash app to explore and update bucket boundaries:
ewb.fit_interactive(df)
#> Dash app running on http://127.0.0.1:8050/
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 |
Metadata
Release files for skorecard 1.6.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| skorecard-1.6.9.tar.gz | 146.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| skorecard-1.6.9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 274.8 kB
Release files / skorecard-1.6.9.tar.gz
| Download URL | skorecard-1.6.9.tar.gz |
|---|---|
| Size | 146.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
7633edfa4d0f421a96a719dc6614543bdce0fa58b14d3cb9df24a0c07bdc4f4e
|
|
BLAKE2b-256 checksum How to use checksums |
717756e6784e5dedbc883dd5c730c269feae9e2f8a89e94d1fb43bc5e56f4f43
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.14
|
Release files / skorecard-1.6.9-py3-none-any.whl
| Download URL | skorecard-1.6.9-py3-none-any.whl |
|---|---|
| Size | 128.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a620d9a707b7a7c7fa8331d168e5bc30acd4b599769b2ecb4b91ee387ffe9a45
|
|
BLAKE2b-256 checksum How to use checksums |
8ffdb5c8ddf9cd2f7f3969019ed46d6d6c80f538c68ca2ef74cc38497f036153
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.14
|