Model Validation

Back to NLP Topics 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.

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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.

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Configure K-fold

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

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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).

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Metrics

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.

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Export

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

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Confusion Matrix

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

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Quadrants

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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.

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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.

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Filter the Graph

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

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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).