confusionMatrix

Given predictions and a target variable, provide numerous statistics from the resulting confusion matrix. The goal is to provide a wealth of summary statistics that can be calculated from a single confusion matrix, and return tidy results with as few dependencies as possible.

library(confusionMatrix)

p = sample(letters[1:2], 250, replace = T, prob = 1:2)
o = sample(letters[1:2], 250, replace = T, prob = 1:2)

result = confusion_matrix(
  prediction = p,
  target = o,
  return_table = TRUE
)

result

$Accuracy
# A tibble: 1 x 6
  Accuracy `Accuracy LL` `Accuracy UL` `Accuracy Guess… `Accuracy P-val…
     <dbl>         <dbl>         <dbl>            <dbl>            <dbl>
1    0.596         0.532         0.657            0.692            0.999
# … with 1 more variable: `Frequency Table` <list>

$Other
# A tibble: 1 x 19
  Positive     N `N Positive` `N Negative` `Sensitivity/Re… `Specificity/TN…
  <chr>    <int>        <int>        <int>            <dbl>            <dbl>
1 a          250           77          173            0.338            0.711
# … with 13 more variables: `PPV/Precision` <dbl>, NPV <dbl>, `F1/Dice` <dbl>,
#   Prevalence <dbl>, `Detection Rate` <dbl>, `Detection Prevalence` <dbl>,
#   `Balanced Accuracy` <dbl>, FDR <dbl>, FOR <dbl>, `FPR/Fallout` <dbl>,
#   FNR <dbl>, `D Prime` <dbl>, AUC <dbl>

$`Association and Agreement`
# A tibble: 1 x 6
   Kappa `Adjusted Rand`  Yule    Phi Peirce Jaccard
   <dbl>           <dbl> <dbl>  <dbl>  <dbl>   <dbl>
1 0.0488          0.0116 0.113 0.0488 0.0486   0.549

result$Accuracy$`Frequency Table`

[[1]]
         Target
Predicted   a   b
        a  26  50
        b  51 123

result = confusion_matrix(
  prediction = p,
  target = o,
  longer = TRUE
)

result

$Accuracy
# A tibble: 5 x 2
  Statistic         Value
  <chr>             <dbl>
1 Accuracy          0.596
2 Accuracy LL       0.532
3 Accuracy UL       0.657
4 Accuracy Guessing 0.692
5 Accuracy P-value  0.999

$Other
# A tibble: 18 x 3
   Positive Statistic                Value
   <chr>    <chr>                    <dbl>
 1 a        N                      250    
 2 a        N Positive              77    
 3 a        N Negative             173    
 4 a        Sensitivity/Recall/TPR   0.338
 5 a        Specificity/TNR          0.711
 6 a        PPV/Precision            0.342
 7 a        NPV                      0.707
 8 a        F1/Dice                  0.340
 9 a        Prevalence               0.308
10 a        Detection Rate           0.104
11 a        Detection Prevalence     0.304
12 a        Balanced Accuracy        0.524
13 a        FDR                      0.658
14 a        FOR                      0.293
15 a        FPR/Fallout              0.289
16 a        FNR                      0.662
17 a        D Prime                  0.137
18 a        AUC                      0.538

$`Association and Agreement`
# A tibble: 6 x 2
  Statistic      Value
  <chr>          <dbl>
1 Kappa         0.0488
2 Adjusted Rand 0.0116
3 Yule          0.113 
4 Phi           0.0488
5 Peirce        0.0486
6 Jaccard       0.549 

Installation

To install from GitHub the devtools package is required.

devtools::install_github('m-clark/confusionMatrix')