| getValidation { RStoolbox} | R Documentation |
Extract validation results from superClass objects
Description
Extract validation results from superClass objects
Usage
getValidation(x, from = "testset", metrics = "overall")
Arguments
x |
superClass object or caret::confusionMatrix |
from |
Character. 'testset' extracts the results from independent validation with testset. 'cv' extracts cross-validation results. |
metrics |
Character. Only relevant in classification mode (ignored for regression models). Select 'overall' for overall accuracy metrics, 'classwise' for classwise metrics, 'confmat' for the confusion matrix itself and 'caret' to return the whole caret::confusionMatrix object. |
Value
Returns a data.frame with validation results. If metrics = 'confmat' or 'caret' will return a table or the full caret::confusionMatrix object, respectively.
Examples
library(pls)
## Fit classifier (splitting training into 70% training data, 30% validation data)
train <- readRDS(system.file("external/trainingPoints.rds", package="RStoolbox"))
SC <- superClass(rlogo, trainData = train, responseCol = "class",
model="pls", trainPartition = 0.7)
## Independent testset-validation
getValidation(SC)
#> model validation Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
#> 1 pls testset 0.8888889 0.8333333 0.5175035 0.9971909 0.3333333
#> AccuracyPValue McnemarPValue
#> 1 0.0009653 NaN
getValidation(SC, metrics = "classwise")
#> model validation class Sensitivity Specificity Pos.Pred.Value Neg.Pred.Value
#> 1 pls testset A 1.0000000 0.8333333 0.75 1.0000000
#> 2 pls testset B 0.6666667 1.0000000 1.00 0.8571429
#> 3 pls testset C 1.0000000 1.0000000 1.00 1.0000000
#> Precision Recall F1 Prevalence Detection.Rate Detection.Prevalence
#> 1 0.75 1.0000000 0.8571429 0.3333333 0.3333333 0.4444444
#> 2 1.00 0.6666667 0.8000000 0.3333333 0.2222222 0.2222222
#> 3 1.00 1.0000000 1.0000000 0.3333333 0.3333333 0.3333333
#> Balanced.Accuracy
#> 1 0.9166667
#> 2 0.8333333
#> 3 1.0000000
## Cross-validation based
getValidation(SC, from = "cv")
#> model validation Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
#> 1 pls cv 0.8571429 0.7857143 0.636576 0.969511 0.3333333
#> AccuracyPValue McnemarPValue
#> 1 1.101588e-06 NaN
[Package RStoolbox version 0.3.0 Index]