Drift Adaptable Models
Adapted Drift Detection Method (DDM) method
Adapted EWMA for Concept Drift Detection (ECDD) method
Adapted Early Drift Detection Method (EDDM) method
Adapted Hoeffding Drift Detection Method (HDDM) method
Inactive dummy detector
KL Distance method
KSWIN method
Mean Comparison Distance method
Multi Criteria Drifter sub-class
Adapted Page Hinkley method
Passive dummy detector
Distribution Based Drifter sub-class
Drifter
Error Based Drifter sub-class
Process Batch
Metric
Accuracy Calculator
FScore Calculator
Precision Calculator
Recall Calculator
ROC AUC Calculator
Multivariate Distribution Based Drifter sub-class
Norm
Memory Normalizer
Reset State
Stealthy
Update State
ADWIN method
Autoencoder-Based Drift Detection method
Cumulative Sum for Concept Drift Detection (CUMSUM) method
In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts. Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.