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name: prediction-evaluation-and-inspection description: "Guides agents using Ludwig model loading, prediction, evaluation, forecasting, generation, visualization, and model inspection workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Prediction, Evaluation, and Inspection

Use this sub-skill when the task starts from a trained Ludwig model or asks for prediction, evaluation, forecasting, generation, model inspection, weights/activations collection, or output interpretation.

Workflow

1. Confirm the model directory has saved weights, hyperparameters/config, and training-set metadata.
2. Confirm the dataset has input columns for prediction and output columns for evaluation.
3. Read workflows.md for CLI and Python recipes.
4. Read api-reference.md for signatures and return shapes.
5. Use the bundled fixture/script for safe data shape work:

python scripts/build_prediction_dataset.py --output /tmp/ludwig-predict.csv
python scripts/inspect_model_artifact.py --model-dir results/experiment_run/model

Command routes

• ludwig predict --model_path MODEL --dataset DATA --output_directory OUT
• ludwig evaluate --model_path MODEL --dataset DATA --output_directory OUT
• ludwig forecast --model_path MODEL --dataset DATA --horizon N
• ludwig inspect --model_path MODEL --json
• ludwig collect_summary, collect_weights, collect_activations

Route elsewhere

• Need to train or resume a model first: training-and-experiments.
• Need to create/validate the config or dataset: configuration-and-data.
• Need a long-running REST endpoint: serving-export-and-deployment.