ResponseBinaryClassif creates a response object that are used as target during the
fitting process.
Format
S4 object.
Usage
ResponseBinaryClassif$new(target_name, pos_class, response)
ResponseBinaryClassif$new(target_name, pos_class, response, weights)Examples
response_binary = ResponseBinaryClassif$new("target", "A", sample(c("A", "B"), 10, TRUE))
response_binary$getResponse()
#> [,1]
#> [1,] 1
#> [2,] -1
#> [3,] 1
#> [4,] 1
#> [5,] 1
#> [6,] 1
#> [7,] -1
#> [8,] -1
#> [9,] 1
#> [10,] -1
response_binary$getPrediction()
#> [,1]
#> [1,] 0
#> [2,] 0
#> [3,] 0
#> [4,] 0
#> [5,] 0
#> [6,] 0
#> [7,] 0
#> [8,] 0
#> [9,] 0
#> [10,] 0
response_binary$getPredictionTransform() # Applies sigmoid to prediction scores
#> [,1]
#> [1,] 0.5
#> [2,] 0.5
#> [3,] 0.5
#> [4,] 0.5
#> [5,] 0.5
#> [6,] 0.5
#> [7,] 0.5
#> [8,] 0.5
#> [9,] 0.5
#> [10,] 0.5
response_binary$getPredictionResponse() # Categorizes depending on the transformed predictions
#> [,1]
#> [1,] 1
#> [2,] 1
#> [3,] 1
#> [4,] 1
#> [5,] 1
#> [6,] 1
#> [7,] 1
#> [8,] 1
#> [9,] 1
#> [10,] 1
response_binary$getTargetName()
#> [1] "target"
response_binary$setThreshold(0.7)
response_binary$getThreshold()
#> [1] 0.7
response_binary$getPositiveClass()
#> [1] "A"