> ## Documentation Index
> Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Validation

<Badge icon="arrow-left" color="gray">[Back to NLP Topics](/ai-for-service/automation/natural-language/nlp-topics)</Badge>

Validate your ML model to estimate its generalization performance and identify training gaps before deploying.

Go to **Natural Language > Training > Validate Model** to access validation options.

***

## K-fold Cross-Validation

Partitions utterances into `k` subsets, trains on `k-1` folds, and tests on the remaining fold — repeated until all utterances are tested at least once.

<Note>
  K-fold Cross-Validation is not available for Few-shot and Zero-shot models.
  Minimum requirements: 5 utterances per intent, 250 utterances total.
</Note>

### Configure K-fold

Set the `k` parameter at **NLU Config > Engine Tuning > K Fold** (value between 2 and 10; default: 2).

### Generate the Report

1. Go to **Natural Language > Training**.
2. Click **Validate Model > K-fold Cross-Validation**.
3. Click **Generate** (first time) or **Re-generate** (subsequent runs).

### Metrics

| Metric        | Description                                                              |
| ------------- | ------------------------------------------------------------------------ |
| **Precision** | Ratio of true positives to total predicted positives. Measures accuracy. |
| **Recall**    | Ratio of true positives to actual positives. Measures coverage.          |
| **F1 Score**  | Weighted average of precision and recall.                                |
| **Mean**      | Average of precision, recall, and F1 across all folds.                   |

Additional context:

* **Total Utterances** — total training corpus size.
* **Number of Intents** — intents in the app.
* **Number of Folds** — k value used.
* **Test/Training Data per Fold** — utterances in each subset.

### Export

Click the **Export** icon on the K-fold page → **Proceed**. File format: `Kfold_BotName_YYYYMMDDHHmmSS.csv`.

***

## Confusion Matrix

Visualizes how well trained utterances match their intended tasks. Each dot represents an utterance, plotted in one of four quadrants per task.

<Note>
  Requires at least one utterance. Only includes intents with utterances in the training dataset.
</Note>

### Quadrants

| Quadrant           | Meaning                                                 | Action                                              |
| ------------------ | ------------------------------------------------------- | --------------------------------------------------- |
| **True Positive**  | Utterance matches its trained intent.                   | Favorable. Higher position = more confidence.       |
| **True Negative**  | Utterance correctly does not match an unrelated intent. | Favorable. Lower position = better separation.      |
| **False Positive** | Utterance incorrectly matches an unrelated intent.      | Fix: retrain the utterance or the competing intent. |
| **False Negative** | Utterance fails to match its trained intent.            | Fix: retrain the utterance or the intent.           |

### Reading the Graph

* Utterances in the top of True quadrants — best match quality.
* Utterances in False quadrants — require immediate attention.
* An utterance in the True Positive quadrant of multiple tasks — overlapping intents; must be resolved.

### Edit and Reassign Utterances

1. Click a quadrant to open the quadrant view.
2. Click the **Edit** icon on an utterance row.
3. Update the text or reassign to a different intent using **Expected Task**.

### Filter the Graph

Filter by **In Development / Published**, **Weak to Strong** or **Strong to Weak** score order, specific intents, or specific utterances.

### Re-run Model

After any changes, click **Re-Run Model** to generate the latest matrix. The platform prompts you to **Train and Regenerate** (if unsaved changes) or **Regenerate** (if model is current).