as.table.confusionMatrix: Save Confusion Table Results

In caret: Classification and Regression Training

Description

Conversion functions for class confusionMatrix

Usage

## S3 method for class 'confusionMatrix'
as.matrix(x, what = "xtabs", ...)

## S3 method for class 'confusionMatrix'
as.table(x, ...)

Arguments

x	an object of class confusionMatrix
what	data to conver to matrix. Either "xtabs", "overall" or "classes"
...	not currently used

Details

For as.table, the cross-tabulations are saved. For as.matrix, the three object types are saved in matrix format.

Value

A matrix or table

Author(s)

Max Kuhn

See Also

confusionMatrix

Examples

###################
## 2 class example

lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
                levels = rev(lvs))
pred <- factor(
               c(
                 rep(lvs, times = c(54, 32)),
                 rep(lvs, times = c(27, 231))),               
               levels = rev(lvs))

xtab <- table(pred, truth)

results <- confusionMatrix(xtab)
as.table(results)
as.matrix(results)
as.matrix(results, what = "overall")
as.matrix(results, what = "classes")
  
###################
## 3 class example

xtab <- confusionMatrix(iris$Species, sample(iris$Species))
as.matrix(xtab)

Example output

Loading required package: lattice
Loading required package: ggplot2
          truth
pred       abnormal normal
  abnormal      231     32
  normal         27     54
         abnormal normal
abnormal      231     32
normal         27     54
                       [,1]
Accuracy       0.8284883721
Kappa          0.5335968379
AccuracyLower  0.7844134380
AccuracyUpper  0.8667985207
AccuracyNull   0.7500000000
AccuracyPValue 0.0003096983
McnemarPValue  0.6025370061
                          [,1]
Sensitivity          0.8953488
Specificity          0.6279070
Pos Pred Value       0.8783270
Neg Pred Value       0.6666667
Precision            0.8783270
Recall               0.8953488
F1                   0.8867562
Prevalence           0.7500000
Detection Rate       0.6715116
Detection Prevalence 0.7645349
Balanced Accuracy    0.7616279
           setosa versicolor virginica
setosa         15         16        19
versicolor     18         16        16
virginica      17         18        15

caret documentation built on May 2, 2019, 5:47 p.m.