Label/target drift

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Definition

The label (target) drift test allows you to check if the labels (targets) have drifted. To compute drift, Openlayer automatically selects the best drift detection method for the label (target) column.

For classification tasks, this test is often referred to as label drift. For regression tasks, it is commonly referred to as target drift.

Drift is measured by comparing a reference dataset with a current dataset.

• In development projects, the training set is used as the reference and the validation set as the current dataset.
• In monitoring projects, the reference dataset is uploaded by the user and the production data is the current dataset.

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Taxonomy

• Category: Consistency.
• Task types: Tabular classification, tabular regression, text classification.
• Availability: development and monitoring.

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Why it matters

• Measuring drift is crucial to maintain the relevance of your models. In development, it allows you to ensure that the data you use to validate your model is similar to the data you used to train it. In monitoring, it allows you to detect when the data your model is receiving is different from the data considered as reference.
• Over time, changes in the underlying data distribution can degrade the performance of your model. Measuring drift helps in identifying these changes early, enabling timely updates or retraining of the model to maintain its performance.

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Related

• Column drift test.
• Feature drift test.