> ## Documentation Index
> Fetch the complete documentation index at: https://docs.abbyy.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Analyze classification results
> Review classification accuracy per class in the Result tab and fix common training-set problems that cause misclassifications.
After training a Classification skill, open the **Result** tab in the Classification Skill Designer to see how accurately the classifier labels each class and to diagnose errors in the training set. Statistics are updated automatically every time the classifier is trained. If accuracy is low, jump to [Classification errors](#classification-errors) for the common causes and how to fix them.
## Prerequisites
* A Classification skill that has been [trained](/vantage/documentation/skill-designer/classification/train-classifier) 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](/vantage/documentation/runtime/analytics-dashboard) and consider [Online learning](/vantage/documentation/runtime/online-learning/enable-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:
Click **Review Prediction in Document Set** in the **Actions** pane, or click the row in the results table.
Select a document that was incorrectly assigned a reference class.
Click the name of the correct class in the **Actions** pane.
Repeat the previous two steps for every document that was incorrectly assigned a reference class.
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
Improve classifier quality by adding the missing documents to the training set. Aim for between 100 and 1,000 documents per class, and include sample documents for the most common variants of each class in roughly a one-to-one ratio.
After you have added your new documents to the training set, assign a class to each and retrain the classifier.
### Confused classes
If two classes are consistently confused because they don't differ meaningfully in shape, layout, or text, merge them into a single class. Separate the documents later in the pipeline using extracted field values if the distinction still matters.
For example, a class for invoices under \$10,000 and a class for invoices over \$10,000 will likely be confused, since the only difference between them is the total amount due. Merge them into one **Invoice** class, and branch on the amount downstream — after the total has been extracted.
## Related topics
Prior step — create a training set, assign classes, and run training.
Continue improving the skill after it's in production.
Track Document Classifier Accuracy over time.