Summarizing Performance for Optimal Bins

Source: R/binning.R

summary method for "optimal_bins". summary metrics to evaluate the performance of binomial classification model.

# S3 method for optimal_bins
summary(object, ...)

Arguments

object

an object of class "optimal_bins", usually, a result of a call to binning_by().
...

further arguments to be passed from or to other methods.

Value

NULL.

Details

print() to print only binning table information of "optimal_bins" objects. summary.performance_bin() includes general metrics and result of significance tests life follows.:

  • Binning Table : Metrics by bins.

    • CntRec, CntPos, CntNeg, RatePos, RateNeg, Odds, WoE, IV, JSD, AUC.

  • General Metrics.

    • Gini index.

    • Jeffrey's Information Value.

    • Jensen-Shannon Divergence.

    • Kolmogorov-Smirnov Statistics.

    • Herfindahl-Hirschman Index.

    • normalized Herfindahl-Hirschman Index.

    • Cramer's V Statistics.

  • Table of Significance Tests.

See also

binning_by, plot.optimal_bins

Examples

# \donttest{
library(dplyr)

# Generate data for the example
heartfailure2 <- heartfailure
heartfailure2[sample(seq(NROW(heartfailure2)), 5), "creatinine"] <- NA

# optimal binning
bin <- binning_by(heartfailure2, "death_event", "creatinine")
#> Warning: The factor y has been changed to a numeric vector consisting of 0 and 1.
#> 'Yes' changed to 1 (positive) and 'No' changed to 0 (negative).
bin
#> binned type: optimal
#> number of bins: 3
#> x
#> [0.5,0.9] (0.9,1.8] (1.8,9.4]      <NA> 
#>        80       167        47         5 

# summary optimal_bins class
summary(bin)
#> ── Binning Table ──────────────────────── Several Metrics ── 
#>         Bin CntRec CntPos CntNeg RatePos RateNeg    Odds      WoE      IV
#> 1 [0.5,0.9]     80      9     71 0.09375 0.34975 0.12676 -1.31660 0.33705
#> 2 (0.9,1.8]    167     51    116 0.53125 0.57143 0.43966 -0.07291 0.00293
#> 3 (1.8,9.4]     47     35     12 0.36458 0.05911 2.91667  1.81930 0.55574
#> 4      <NA>      5      1      4 0.01042 0.01970 0.25000 -0.63744 0.00592
#> 5     Total    299     96    203 1.00000 1.00000 0.47291       NA 0.90164
#>       JSD     AUC
#> 1 0.03933 0.01639
#> 2 0.00037 0.20536
#> 3 0.06124 0.04772
#> 4 0.00073 0.01960
#> 5 0.10166 0.28908
#> 
#> ── General Metrics ───────────────────────────────────────── 
#> • Gini index                       :  -0.42185
#> • IV (Jeffrey)                     :  0.90164
#> • JS (Jensen-Shannon) Divergence   :  0.10166
#> • Kolmogorov-Smirnov Statistics    :  0.29618
#> • HHI (Herfindahl-Hirschman Index) :  0.40853
#> • HHI (normalized)                 :  0.21137
#> • Cramer's V                       :  0.43116 
#> 
#> ── Significance Tests ──────────────────── Chisquare Test ── 
#>       Bin A     Bin B statistics      p_value
#> 1 [0.5,0.9] (0.9,1.8]   10.94275 9.397059e-04
#> 2 (0.9,1.8] (1.8,9.4]   29.44595 5.749814e-08
#> 

# performance table
attr(bin, "performance")
#>         Bin CntRec CntPos CntNeg CntCumPos CntCumNeg RatePos RateNeg RateCumPos
#> 1 [0.5,0.9]     80      9     71         9        71 0.09375 0.34975    0.09375
#> 2 (0.9,1.8]    167     51    116        60       187 0.53125 0.57143    0.62500
#> 3 (1.8,9.4]     47     35     12        95       199 0.36458 0.05911    0.98958
#> 4      <NA>      5      1      4        96       203 0.01042 0.01970    1.00000
#> 5     Total    299     96    203        NA        NA 1.00000 1.00000         NA
#>   RateCumNeg    Odds   LnOdds      WoE      IV     JSD     AUC
#> 1    0.34975 0.12676 -2.06546 -1.31660 0.33705 0.03933 0.01639
#> 2    0.92118 0.43966 -0.82176 -0.07291 0.00293 0.00037 0.20536
#> 3    0.98030 2.91667  1.07044  1.81930 0.55574 0.06124 0.04772
#> 4    1.00000 0.25000 -1.38629 -0.63744 0.00592 0.00073 0.01960
#> 5         NA 0.47291 -0.74886       NA 0.90164 0.10166 0.28908

# visualize all information for optimal_bins class
plot(bin)

# visualize WoE information for optimal_bins class
plot(bin, type = "WoE")

# visualize all information without typographic
plot(bin, typographic = FALSE)

# extract binned results
extract(bin) %>%
  head(20)
#>  [1] (1.8,9.4] (0.9,1.8] (0.9,1.8] (1.8,9.4] (1.8,9.4] (1.8,9.4] (0.9,1.8]
#>  [8] (0.9,1.8] (0.9,1.8] (1.8,9.4] (1.8,9.4] [0.5,0.9] (0.9,1.8] (0.9,1.8]
#> [15] (0.9,1.8] (0.9,1.8] [0.5,0.9] [0.5,0.9] (0.9,1.8] (1.8,9.4]
#> Levels: [0.5,0.9] < (0.9,1.8] < (1.8,9.4]
# }