Tools for binning data
Installation
# Install rbin from CRAN install.packages("rbin") # Or the development version from GitHub # install.packages("devtools") devtools::install_github("rsquaredacademy/rbin")
Usage
Manual Binning
bins <- rbin_manual(mbank, y, age, c(29, 31, 34, 36, 39, 42, 46, 51, 56)) bins #> Binning Summary #> --------------------------- #> Method Manual #> Response y #> Predictor age #> Bins 10 #> Count 4521 #> Goods 517 #> Bads 4004 #> Entropy 0.5 #> Information Value 0.12 #> #> #> cut_point bin_count good bad woe iv entropy #> 1 < 29 410 71 339 -0.483686036 2.547353e-02 0.6649069 #> 2 < 31 313 41 272 -0.154776266 1.760055e-03 0.5601482 #> 3 < 34 567 55 512 0.183985174 3.953685e-03 0.4594187 #> 4 < 36 396 45 351 0.007117468 4.425063e-06 0.5107878 #> 5 < 39 519 47 472 0.259825118 7.008270e-03 0.4383322 #> 6 < 42 431 33 398 0.442938178 1.575567e-02 0.3899626 #> 7 < 46 449 47 402 0.099298221 9.423907e-04 0.4836486 #> 8 < 51 521 40 481 0.439981550 1.881380e-02 0.3907140 #> 9 < 56 445 49 396 0.042587647 1.756117e-04 0.5002548 #> 10 >= 56 470 89 381 -0.592843261 4.564428e-02 0.7001343 # plot plot(bins)
Combine Factor Levels
# combine levels upper <- c("secondary", "tertiary") out <- rbin_factor_combine(mbank, education, upper, "upper") table(out$education) #> #> upper unknown primary #> 3651 179 691 # bins bins <- rbin_factor(out, y, education) bins #> Binning Summary #> --------------------------- #> Method Custom #> Response y #> Predictor education #> Levels 3 #> Count 4521 #> Goods 517 #> Bads 4004 #> Entropy 0.51 #> Information Value 0.01 #> #> #> level bin_count good bad woe iv entropy #> 1 upper 3651 426 3225 -0.02275738 0.0004219212 0.5197428 #> 2 primary 691 66 625 0.20109064 0.0057178780 0.4546110 #> 3 unknown 179 25 154 -0.22892949 0.0022651110 0.5833603 # plot plot(bins)
Quantile Binning
bins <- rbin_quantiles(mbank, y, age, 10) bins #> Binning Summary #> ----------------------------- #> Method Quantile #> Response y #> Predictor age #> Bins 10 #> Count 4521 #> Goods 517 #> Bads 4004 #> Entropy 0.5 #> Information Value 0.12 #> #> #> cut_point bin_count good bad woe iv entropy #> 1 < 29 410 71 339 -0.483686036 2.547353e-02 0.6649069 #> 2 < 31 313 41 272 -0.154776266 1.760055e-03 0.5601482 #> 3 < 34 567 55 512 0.183985174 3.953685e-03 0.4594187 #> 4 < 36 396 45 351 0.007117468 4.425063e-06 0.5107878 #> 5 < 39 519 47 472 0.259825118 7.008270e-03 0.4383322 #> 6 < 42 431 33 398 0.442938178 1.575567e-02 0.3899626 #> 7 < 46 449 47 402 0.099298221 9.423907e-04 0.4836486 #> 8 < 51 521 40 481 0.439981550 1.881380e-02 0.3907140 #> 9 < 56 445 49 396 0.042587647 1.756117e-04 0.5002548 #> 10 >= 56 470 89 381 -0.592843261 4.564428e-02 0.7001343 # plot plot(bins)
Winsorized Binning
bins <- rbin_winsorize(mbank, y, age, 10, winsor_rate = 0.05) bins #> Binning Summary #> ------------------------------ #> Method Winsorize #> Response y #> Predictor age #> Bins 10 #> Count 4521 #> Goods 517 #> Bads 4004 #> Entropy 0.51 #> Information Value 0.1 #> #> #> cut_point bin_count good bad woe iv entropy #> 1 < 30.2 723 112 611 -0.3504082 0.0224390979 0.6219926 #> 2 < 33.4 567 55 512 0.1839852 0.0039536848 0.4594187 #> 3 < 36.6 573 58 515 0.1367176 0.0022470488 0.4728562 #> 4 < 39.8 497 44 453 0.2846962 0.0079801719 0.4315480 #> 5 < 43 396 37 359 0.2253982 0.0040782670 0.4478305 #> 6 < 46.2 461 43 418 0.2272751 0.0048235624 0.4473095 #> 7 < 49.4 281 22 259 0.4187793 0.0092684760 0.3961315 #> 8 < 52.6 309 32 277 0.1112753 0.0008106706 0.4801796 #> 9 < 55.8 244 25 219 0.1231896 0.0007809490 0.4767424 #> 10 >= 55.8 470 89 381 -0.5928433 0.0456442813 0.7001343 # plot plot(bins)
Equal Length Binning
bins <- rbin_equal_length(mbank, y, age, 10) bins #> Binning Summary #> --------------------------------- #> Method Equal Length #> Response y #> Predictor age #> Bins 10 #> Count 4521 #> Goods 517 #> Bads 4004 #> Entropy 0.5 #> Information Value 0.17 #> #> #> cut_point bin_count good bad woe iv entropy #> 1 < 24.6 85 24 61 -1.11418623 0.0347480126 0.8586371 #> 2 < 31.2 822 106 716 -0.13676519 0.0035843196 0.5545619 #> 3 < 37.8 1133 115 1018 0.13365680 0.0042514380 0.4737339 #> 4 < 44.4 943 82 861 0.30436899 0.0171748162 0.4262287 #> 5 < 51 623 52 571 0.34913923 0.0146733167 0.4142794 #> 6 < 57.6 612 66 546 0.06595797 0.0005741022 0.4933757 #> 7 < 64.2 229 43 186 -0.58245971 0.0213871054 0.6967893 #> 8 < 70.8 34 12 22 -1.44087046 0.0255269312 0.9366674 #> 9 < 77.4 25 13 12 -2.12704897 0.0471100183 0.9988455 #> 10 >= 77.4 15 4 11 -1.03540535 0.0051663529 0.8366407 # plot plot(bins)
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.