Intro & Install

Introduction

A High-level Scorecard Modeling API

Scorecard-Bundle is a high-level Scorecard modeling API that is easy-to-use and Scikit-Learn consistent. It covers the major steps to train a Scorecard model such as feature discretization with ChiMerge, WOE encoding, feature evaluation with information value and collinearity, Logistic-Regression-based Scorecard model, model evaluation for binary classification tasks, and model interpretation tools for Scorecard. All the transformer and model classes in Scorecard-Bundle comply with Scikit-Learn‘s fit-transform-predict convention.

A complete example showing how to build a scorecard with Scorecard-Bundle: Example Notebooks;

Detailed documentation about the package: API Reference;

In Scorecard-Bundle, core codes such as WOE/IV calculation and scorecard transformation were written based on Mamdouh Refaat's book ‘"Credit Risk Scorecards: Development and Implementation Using SAS"；ChiMerge was written based on Randy Kerber's paper "ChiMerge: Discretization of Numeric Attributes".

Installation

Installing the latest version is strongly recommended as every version either corrected known bugs or added useful functionality. In principle, critical bugs are fixed as soon as they are revealed. Therefore please file an issue on Github if you suspect the presence of a bug when using Scorecard-Bundle.

Note that Scorecard-Bundle depends on NumPy, Pandas, matplotlib, Scikit-Learn, and SciPy, which can be installed individually or together through Anaconda

• Pip: Scorecard-Bundle can be installed with pip: pip install --upgrade scorecardbundle

  ！Note that the latest version may be not available at some pip mirror site (e.g. https://mirrors.aliyun.com/pypi/simple/). Therefore in order to update to the latest version, use the following command to specify the source as https://pypi.org/project

  pip install -i https://pypi.org/project --upgrade scorecardbundle
  

  Import packages:

  from scorecardbundle.feature_discretization import ChiMerge as cm
  from scorecardbundle.feature_discretization import FeatureIntervalAdjustment as fia
  from scorecardbundle.feature_encoding import WOE as woe
  from scorecardbundle.feature_selection import FeatureSelection as fs
  from scorecardbundle.model_training import LogisticRegressionScoreCard as lrsc
  from scorecardbundle.model_evaluation import ModelEvaluation as me
  from scorecardbundle.model_interpretation import ScorecardExplainer as mise
  

• Manually: Download codes from github <https://github.com/Lantianzz/Scorecard-Bundle> and import them directly:

  import sys
  sys.path.append('E:\Github\Scorecard-Bundle') # add path that contains the codes
  from scorecardbundle.feature_discretization import ChiMerge as cm
  from scorecardbundle.feature_discretization import FeatureIntervalAdjustment as fia
  from scorecardbundle.feature_encoding import WOE as woe
  from scorecardbundle.feature_selection import FeatureSelection as fs
  from scorecardbundle.model_training import LogisticRegressionScoreCard as lrsc
  from scorecardbundle.model_evaluation import ModelEvaluation as me
  from scorecardbundle.model_interpretation import ScorecardExplainer as mise