Data Drift
Last updated
Last updated
© 2024 Fiddler Labs, Inc.
Model performance can be poor if models trained on a specific dataset encounter different data in production. This is called data drift.
Track data drift for inputs, outputs, and custom features
Fiddler supports the following:
Drift Metrics
Jensen–Shannon distance (JSD)
A distance metric calculated between the distribution of a field in the baseline dataset and that same distribution for the time period of interest.
For more information on JSD, click here.
Population Stability Index (PSI)
A drift metric based on the multinomial classification of a variable into bins or categories. The differences in each bin between the baseline and the time period of interest are then utilized to calculate it as follows:
🚧 Note
There is a possibility that PSI can shoot to infinity. To avoid this, PSI calculation in Fiddler is done such that each bin count is incremented with a base_count=1. Thus, there might be a slight difference in the PSI values obtained from manual calculations.
Average Values – The mean of a field (feature or prediction) over time. This can be thought of as an intuitive drift score.
Drift Analytics – You can drill down into the features responsible for the prediction drift using the table at the bottom.
Feature Impact: The contribution of a feature to the model’s predictions, averaged over the baseline dataset. The contribution is calculated using random ablation feature impact.
Feature Drift: Drift of the feature, calculated using the drift metric of choice.
Prediction Drift Impact: A heuristic calculated using the product of the feature impact and the feature drift. The higher the score, the more this feature is likely to have contributed to the prediction drift.
Data drift is a great proxy metric for performance decline, especially if there is delay in getting labels for production events. (e.g. In a credit lending use case, an actual default may happen after months or years.)
Monitoring data drift also helps you stay informed about distributional shifts in the data for features of interest, which could have business implications even if there is no decline in model performance.
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