stealthy: Stealthy

In heimdall: Drift Adaptable Models

Stealthy

Description

Ancestor class for drift adaptive models

Usage

stealthy(
  model,
  drift_method,
  monitored_features = NULL,
  norm_class = daltoolbox::zscore(),
  warmup_size = 100,
  th = 0.5,
  target_uni_drifter = FALSE,
  incremental_memory = TRUE,
  verbose = FALSE,
  reporting = FALSE
)

Arguments

model	The algorithm object to be used for predictions
drift_method	The algorithm object to detect drifts
monitored_features	List of features that will be monitored by the drifter
norm_class	Class used to perform normalization
warmup_size	Number of rows used to warmup the drifter. No drift will be detected during this phase
th	The threshold to be used with classification algorithms
target_uni_drifter	Passes the prediction target to the drifts as the target feat when the drifter is univariate and dist_based.
incremental_memory	If true, the model will retrain with all available data whenever the fit is called. If false, it only retrains when a drift is detected.
verbose	if TRUE shows drift messages
reporting	If TRUE, some data are returned as norm_x_oh, drift_input, hist_proj, and recent_proj.

Value

Stealthy object

Examples

# See ?dd_ddm for an example of DDM drift detector

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