Synthetic datasets for concept drift detection purposes
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'In order to know about the performance of a drift detector by measuring the different detection metrics, we need to know beforehand where a real drift occurs. This is only possible with synthetic datasets. The scikit-multiflow framework allows generating several kinds of synthetic data to simulate the occurrence of drifts' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["concept drift"]} <|eot_id|>
 Here is the Description of a dataset. Description:'In order to know about the performance of a drift detector by measuring the different detection metrics, we need to know beforehand where a real drift occurs. This is only possible with synthetic datasets. The scikit-multiflow framework allows generating several kinds of synthetic data to simulate the occurrence of drifts' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
In order to know about the performance of a drift detector by measuring the different detection metrics, we need to know beforehand where a real drift occurs. This is only possible with synthetic datasets. The scikit-multiflow framework allows generating several kinds of synthetic data to simulate the occurrence of drifts
concept drift