Summarizing Performance for Binned Variable

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

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

Arguments

object	an object of class "performance_bin", usually, a result of a call to performance_bin().
...	further arguments to be passed from or to other methods.

Value

NULL.

Details

print() to print only binning table information of "performance_bin" 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.

Examples

# \donttest{
# Generate data for the example
heartfailure2 <- heartfailure

set.seed(123)
heartfailure2[sample(seq(NROW(heartfailure2)), 5), "creatinine"] <- NA

# Change the target variable to 0(negative) and 1(positive).
heartfailure2$death_event_2 <- ifelse(heartfailure2$death_event %in% "Yes", 1, 0)

# Binnig from creatinine to platelets_bin.
breaks <- c(0,  1,  2, 10)
heartfailure2$creatinine_bin <- cut(heartfailure2$creatinine, breaks)

# Diagnose performance binned variable
perf <- performance_bin(heartfailure2$death_event_2, heartfailure2$creatinine_bin)
perf
#>      Bin CntRec CntPos CntNeg CntCumPos CntCumNeg RatePos RateNeg RateCumPos
#> 1  (0,1]    128     24    104        24       104 0.25000 0.51232    0.25000
#> 2  (1,2]    132     51     81        75       185 0.53125 0.39901    0.78125
#> 3 (2,10]     34     21     13        96       198 0.21875 0.06404    1.00000
#> 4   <NA>      5      0      5        96       203 0.00000 0.02463    1.00000
#> 5  Total    299     96    203        NA        NA 1.00000 1.00000         NA
#>   RateCumNeg    Odds   LnOdds      WoE      IV     JSD     AUC
#> 1    0.51232 0.23077 -1.46634 -0.71748 0.18821 0.02303 0.06404
#> 2    0.91133 0.62963 -0.46262  0.28623 0.03785 0.00472 0.20574
#> 3    0.97537 1.61538  0.47957  1.22843 0.19005 0.02237 0.05704
#> 4    1.00000 0.00000     -Inf       NA      NA      NA 0.02463
#> 5         NA 0.47291 -0.74886       NA      NA      NA 0.35145
summary(perf)
#> ── Binning Table ──────────────────────── Several Metrics ── 
#>      Bin CntRec CntPos CntNeg RatePos RateNeg    Odds      WoE      IV     JSD
#> 1  (0,1]    128     24    104 0.25000 0.51232 0.23077 -0.71748 0.18821 0.02303
#> 2  (1,2]    132     51     81 0.53125 0.39901 0.62963  0.28623 0.03785 0.00472
#> 3 (2,10]     34     21     13 0.21875 0.06404 1.61538  1.22843 0.19005 0.02237
#> 4   <NA>      5      0      5 0.00000 0.02463 0.00000       NA      NA      NA
#> 5  Total    299     96    203 1.00000 1.00000 0.47291       NA      NA      NA
#>       AUC
#> 1 0.06404
#> 2 0.20574
#> 3 0.05704
#> 4 0.02463
#> 5 0.35145
#> 
#> ── General Metrics ───────────────────────────────────────── 
#> • Gini index                       :  -0.29711
#> • IV (Jeffrey)                     :  NA
#> • JS (Jensen-Shannon) Divergence   :  NA
#> • Kolmogorov-Smirnov Statistics    :  0.26232
#> • HHI (Herfindahl-Hirschman Index) :  0.39137
#> • HHI (normalized)                 :  0.1885
#> • Cramer's V                       :  0.30024 
#> 
#> ── Significance Tests ──────────────────── Chisquare Test ── 
#>   Bin A  Bin B statistics      p_value
#> 1 (0,1]  (1,2]  12.520885 0.0004024283
#> 2 (1,2] (2,10]   5.888296 0.0152418462
#> 

# plot(perf)

# Diagnose performance binned variable without NA
perf <- performance_bin(heartfailure2$death_event_2, heartfailure2$creatinine_bin, na.rm = TRUE)
perf
#>      Bin CntRec CntPos CntNeg CntCumPos CntCumNeg RatePos RateNeg RateCumPos
#> 1  (0,1]    128     24    104        24       104 0.25000 0.52525    0.25000
#> 2  (1,2]    132     51     81        75       185 0.53125 0.40909    0.78125
#> 3 (2,10]     34     21     13        96       198 0.21875 0.06566    1.00000
#> 4  Total    294     96    198        NA        NA 1.00000 1.00000         NA
#>   RateCumNeg    Odds   LnOdds      WoE      IV     JSD     AUC
#> 1    0.52525 0.23077 -1.46634 -0.74242 0.20435 0.02497 0.06566
#> 2    0.93434 0.62963 -0.46262  0.26130 0.03192 0.00398 0.21094
#> 3    1.00000 1.61538  0.47957  1.20349 0.18425 0.02173 0.05848
#> 4         NA 0.48485 -0.72392       NA 0.42052 0.05069 0.33507
summary(perf)
#> ── Binning Table ──────────────────────── Several Metrics ── 
#>      Bin CntRec CntPos CntNeg RatePos RateNeg    Odds      WoE      IV     JSD
#> 1  (0,1]    128     24    104 0.25000 0.52525 0.23077 -0.74242 0.20435 0.02497
#> 2  (1,2]    132     51     81 0.53125 0.40909 0.62963  0.26130 0.03192 0.00398
#> 3 (2,10]     34     21     13 0.21875 0.06566 1.61538  1.20349 0.18425 0.02173
#> 4  Total    294     96    198 1.00000 1.00000 0.48485       NA 0.42052 0.05069
#>       AUC
#> 1 0.06566
#> 2 0.21094
#> 3 0.05848
#> 4 0.33507
#> 
#> ── General Metrics ───────────────────────────────────────── 
#> • Gini index                       :  -0.32986
#> • IV (Jeffrey)                     :  0.42052
#> • JS (Jensen-Shannon) Divergence   :  0.05069
#> • Kolmogorov-Smirnov Statistics    :  0.27525
#> • HHI (Herfindahl-Hirschman Index) :  0.40451
#> • HHI (normalized)                 :  0.10676
#> • Cramer's V                       :  0.30024 
#> 
#> ── Significance Tests ──────────────────── Chisquare Test ── 
#>   Bin A  Bin B statistics      p_value
#> 1 (0,1]  (1,2]  12.520885 0.0004024283
#> 2 (1,2] (2,10]   5.888296 0.0152418462
#> 

plot(perf)
# }

Arguments

Value

Details

See also

Examples