dfr_mcdd: Mean Comparison Distance method

In heimdall: Drift Adaptable Models

Mean Comparison Distance method

Description

Mean Comparison statistical method for concept drift detection.

Usage

dfr_mcdd(target_feat = NULL, alpha = 1e-08, window_size = 1500)

Arguments

target_feat	Feature to be monitored
alpha	Probability theshold for all test statistics
window_size	Size of the sliding window

Value

dfr_mcdd object

Examples

library(daltoolbox)
library(heimdall)

# This example uses a dist-based drift detector with a synthetic dataset.

data(st_drift_examples)
data <- st_drift_examples$univariate
data$event <- NULL

model <- dfr_mcdd(target_feat='depart_visibility')

detection <- NULL
output <- list(obj=model, drift=FALSE)
for (i in 1:length(data$serie)){
 output <- update_state(output$obj, data$serie[i])
 if (output$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',]

heimdall documentation built on June 8, 2025, 11:11 a.m.