The Attribution Model Error Rate Estimator measures how often an attribution model fails a defined validation check. Errors might include assigning credit to the wrong channel, missing an eligible touchpoint, or disagreeing with a reviewed benchmark, as long as the same error rule is applied to every evaluated case. Use the result as a straightforward quality-monitoring metric. In addition to the error rate, the page shows the complementary accuracy rate and, when you provide a target, the difference between the observed error rate and that target. This makes repeated validation batches easier to compare on a consistent basis.
1. Set the evaluation volume
Enter how many attribution outcomes were checked in the batch.
2. Record observed errors
Count only cases that fail the same predefined attribution-quality rule.
3. Add a target if useful
Enter an optional target error rate to see whether the batch is above or below that threshold.
4. Read the quality metrics
Review the error rate first, then use the complementary accuracy and target difference for context.
Where:
• errors = number of evaluated outcomes classified as incorrect
• evaluated outcomes = total cases reviewed using the same rule
• target difference = observed error rate − optional target rate
Assumptions: Each reviewed case is counted once and is classified consistently as either an error or not an error. The calculator reports an observed rate; it does not infer statistical significance.
A lower error rate indicates fewer validation failures under the rule you used. The target difference is positive when the observed rate is above target and negative when it is below target.
Changes in sampling, ground-truth labels, attribution windows, or the error definition can make rates from different batches non-comparable.
Given: 1,500 evaluated conversions, 96 attribution errors, and a 5% target.
Calculation: Error rate = 96 / 1,500 × 100 = 6.40%. Accuracy = 100% − 6.40% = 93.60%. Difference vs target = 6.40% − 5.00% = +1.40 percentage points.
Result: 6.40% observed error rate.
Interpretation: The batch is 1.40 percentage points above the stated 5% target, so the underlying error cases merit review before treating the target as met.
For a binary pass/fail validation rule, yes: accuracy is 100% minus the error rate. More complex multiclass metrics can require separate definitions.
Only if duplicates are intentionally part of the evaluation population. Otherwise deduplicate according to the same validation protocol used to define the total sample.
No. A model can have a low observed error rate under one test and still contain systematic bias, coverage gaps, or an unsuitable attribution assumption.
Zero is treated as no comparison target, so the target-difference row displays “Not set.” The observed error rate is still calculated normally.
Use a confidence interval when you need to express sampling uncertainty around an observed proportion. Use this estimator when the primary need is a direct operational error-rate calculation.