Prerequisites
- A Classification skill that has been trained at least once.
What the Result tab shows
- General classification accuracy — percentage of correctly classified documents across the full set.
- Per-class accuracy — percentage of documents classified correctly for each class.
- Per-class document counts — number of correctly and incorrectly classified documents per class.
- Last trained — time and date of the most recent training run.
Results table
The results table contains all non-empty user classes (excluding No class). Classes are sorted first by accuracy (worst to best), then by document count, and finally alphabetically by name. A scrollbar appears if all rows don’t fit on screen. Clicking a row opens the corresponding class in the Documents tab. Renaming a class in the Documents tab updates the name in the Result tab automatically. If you delete a class after training, its name appears grayed out in the Result tab; the row is removed only the next time the classifier is trained.When to stop iterating
There is no fixed accuracy threshold for a Classification skill — the right target depends on your downstream tolerance for misrouted documents and how much manual review is acceptable. As a practical guide, aim for high per-class accuracy (not just overall), iterate on the causes below while the gap is closing, and stop once a class either meets your business requirement or has clearly plateaued despite rebalanced, clean training data. If a class plateaus well below the others, treat it as indistinguishable and merge it with its nearest neighbor. Once the skill is in production, continue tracking Document Classifier Accuracy over time in the Analytics Dashboard and consider Online learning for continuous improvement.Classification errors
Most cases of incorrect classification are caused by errors in the training set — for example, incorrectly assigned reference classes or an insufficient number of documents for a given class.Incorrectly assigned reference classes
To fix this, reassign affected documents and retrain:1
Open the affected class in the Documents tab
Click Review Prediction in Document Set in the Actions pane, or click the row in the results table.
2
Select a misclassified document
Select a document that was incorrectly assigned a reference class.
3
Assign the correct class
Click the name of the correct class in the Actions pane.
4
Repeat for every affected document
Repeat the previous two steps for every document that was incorrectly assigned a reference class.
5
Retrain the classifier
Click the Train button in the Actions pane.
Insufficient or imbalanced training data
Insufficient classifier quality may be caused by the following:- An insufficient number of uploaded documents
- A substantially uneven distribution of documents among classes
- An insufficient number of samples of the most common document variants for the given class