dfr_cusum {heimdall} | R Documentation |
Cumulative Sum for Concept Drift Detection (CUMSUM) method
Description
The cumulative sum (CUSUM) is a sequential analysis technique used for change detection.
Usage
dfr_cusum(lambda = 100)
Arguments
lambda |
Necessary level for warning zone (2 standard deviation) |
Value
dfr_cusum
object
Examples
library(daltoolbox)
library(heimdall)
# This example uses an error-based drift detector with a synthetic a
# model residual where 1 is an error and 0 is a correct prediction.
data(st_drift_examples)
data <- st_drift_examples$univariate
data$event <- NULL
data$prediction <- st_drift_examples$univariate$serie > 4
model <- dfr_cusum()
detection <- NULL
output <- list(obj=model, drift=FALSE)
for (i in 1:length(data$prediction)){
output <- update_state(output$obj, data$prediction[i])
if (output$drift){
type <- 'drift'
output$obj <- reset_state(output$obj)
}else{
type <- ''
}
detection <- rbind(detection, data.frame(idx=i, event=output$drift, type=type))
}
detection[detection$type == 'drift',]
[Package heimdall version 1.0.717 Index]