Output drift in Watson OpenScale drift v2 metrics
Watson OpenScale calculates output drift by measuring the change in the model confidence distribution.
How it works
Watson OpenScale measures how much your model output changes from the time that you train the model. For regression models, Watson OpenScale calculates output drift by measuring the change in distribution of predictions on the training and payload data. For classification models, Watson OpenScale calculates output drift for each class probability by measuring the change in distribution for class probabilities on the training and payload data. For multi-classification models, Watson OpenScale also aggregates output drift for each class probability by measuring a weighted average.
Do the math
Watson OpenScale uses the following formulas to calculate output drift:
Total variation distance
Total variation distance measures the maximum difference between the probabilities that two probability distributions, baseline (B) and production (P), assign to the same transaction as shown in the following formula:
If the two distributions are equal, the total variation distance between them becomes 0.
Watson OpenScale uses the following formula to calculate total variation distance:
-
π₯ is a series of equidistant samples that span the domain of
that range from the combined miniumum of the baseline and production data to the combined maximum of the
baseline and production data. -
is the difference between two consecutive π₯ samples. -
is the value of the density function for production data at a π₯ sample. -
is the value of the density function for baseline data for at a π₯ sample.
The 
denominator represents the total area under the density function plots for production and baseline data. These summations are an approximation of the integrations
over the domain space and both these terms should be 1 and total should be 2.
Overlap coefficient
Watson OpenScale calculates the overlap coefficient by measuring the total area of the intersection between two probability distributions. To measure dissimilarity between distributions, the intersection or the overlap area is subtracted from 1 to calculate the amount of drift. Watson OpenScale uses the following formula to calculate the overlap coefficient:
-
π₯ is a series of equidistant samples that span the domain of
that range from the combined miniumum of the baseline and production data to the combined maximum of the
baseline and production data. -
is the difference between two consecutive π₯ samples.
-
is the value of the density function for production data at a π₯ sample.
-
is the value of the density function for baseline data for at a π₯ sample.
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Parent topic: Drift v2 metrics