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ben519
mltools:Machine Learning Tools
A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
Maintained by Ben Gorman. Last updated 4 years ago.
exploratory-data-analysismachine-learning
72 stars 9.67 score 1.2k scripts 13 dependents
shichenxie
scorecard:Credit Risk Scorecard
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.
Maintained by Shichen Xie. Last updated 12 months ago.
binningcredit-scoringreleasescorecardwoewoebinning
164 stars 8.07 score 94 scripts
myles-lewis
nestedcv:Nested Cross-Validation with 'glmnet' and 'caret'
Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
Maintained by Myles Lewis. Last updated 12 days ago.
12 stars 7.90 score 46 scripts
chadhazlett
kbal:Kernel Balancing
The "kbal" package provides a weighting approach that employs kernels to make one group have a similar distribution to another group on covariates, not only in terms of means or marginal distributions, but also on higher order transformations implied by the choice of kernel. The package is applicable to both treatment effect estimation and survey reweighting problems. Based on Hazlett, C. (2020) "Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects." Statistica Sinica. <https://www.researchgate.net/publication/299013953_Kernel_Balancing_A_flexible_non-parametric_weighting_procedure_for_estimating_causal_effects/stats>.
Maintained by Borna Bateni. Last updated 7 months ago.
11 stars 5.04 score