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The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.

copied from cf-post-staging / r-scorecard
Type Size Name Uploaded Downloads Labels
conda 369.5 kB | noarch/r-scorecard-0.4.6-r45h785f33e_0.conda  16 days and 10 hours ago 80 main
conda 368.4 kB | noarch/r-scorecard-0.4.6-r44h785f33e_0.conda  16 days and 10 hours ago 81 main

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