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After training your triage models, you can run batch predictions on new ticket data. This guide explains how to use the prediction script to classify tickets and export results.

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Prerequisites

  • Trained models in artifacts/ directory (see Training Models)
  • Test data: tickets_test.csv in the project root
  • Required columns: subject, body

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Prediction Command

1

Ensure models are trained

Verify that model files exist:
ls artifacts/
# Expected output:
# category_model.joblib
# priority_model.joblib
2

Prepare test data

Create or verify tickets_test.csv with at minimum these columns:
  • subject: Ticket subject line
  • body: Ticket description or body text
3

Run predictions

uv run -m src.ml.predict
The script will:
  • Load trained models from artifacts/
  • Preprocess test data
  • Run category and priority predictions
  • Compute confidence scores
  • Save results to CSV
4

Check results

Starting prediction...
Prediction complete.
Results are saved to reports/predictions.csv.

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Output Format

The prediction script generates a CSV file with the original test data plus four additional columns:
ColumnDescription
predicted_categoryPredicted support category (e.g., “billing”, “technical”)
category_confidenceConfidence score (0.0 to 1.0) for category prediction
predicted_priorityPredicted priority level (e.g., “low”, “medium”, “high”)
priority_confidenceConfidence score (0.0 to 1.0) for priority prediction

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Example Output

subject,body,predicted_category,category_confidence,predicted_priority,priority_confidence
"Billing issue","I was charged twice",billing,0.92,high,0.87
"Login error","Cannot access my account",technical,0.88,medium,0.75
"Feature request","Can you add dark mode?",feature_request,0.81,low,0.69

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How Predictions Work

The prediction pipeline executes the following steps (see src/ml/predict.py:125):
  1. Load Models: Loads trained pipelines from artifacts/ directory
  2. Read Test Data: Loads tickets_test.csv into a DataFrame
  3. Preprocess: Normalizes text (lowercase, strip whitespace, fill NaN values)
  4. Feature Extraction: Applies TF-IDF transformations via trained pipelines
  5. Predict: Generates category and priority predictions
  6. Confidence Scores: Computes max probability from predict_proba
  7. Export: Saves augmented DataFrame to reports/predictions.csv

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Confidence Scores

Confidence scores indicate model certainty (see src/ml/predict.py:73):
  • High confidence (> 0.8): Model is very certain
  • Medium confidence (0.5 - 0.8): Moderate certainty
  • Low confidence (< 0.5): Uncertain prediction, may require manual review
Note: If a model is a ConstantPredictor (single-class fallback), confidence is always 1.0.

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Custom Prediction Paths

You can customize input/output paths programmatically: 
from src.ml.predict import predict_and_save

predict_and_save(
    artifacts_dir="path/to/artifacts",
    test_csv="path/to/test.csv",
    output_file="path/to/output.csv",
    preprocess=True
)

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Using Predictions in API

The trained models are automatically loaded by the /triage API endpoint: 
curl -X POST "http://localhost:8000/triage" \
  -H "Content-Type: application/json" \
  -d '{
    "subject": "Billing issue",
    "body": "I was charged twice"
  }'
Response: 
{
  "category": "billing",
  "priority": "high",
  "category_confidence": 0.92,
  "priority_confidence": 0.87
}

​
Preprocessing Behavior

The prediction script applies lightweight preprocessing (see src/ml/predict.py:46):
  • Convert text to lowercase
  • Strip leading/trailing whitespace
  • Fill NaN values with empty strings
This mirrors the training-time preprocessing to ensure consistency.

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Troubleshooting

Train models first using:
uv run -m src.ml.train
Ensure your test CSV has both subject and body columns. Other columns are optional.
This may indicate:
  • Test data distribution differs from training data
  • Models need retraining with more diverse examples
  • Feature vocabulary mismatch
This occurs when models were trained on single-class data and fell back to ConstantPredictor.

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