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name: model-training description: "Train and evaluate DeepChem models; choose metrics, model families, hyperparameter search, save/restore, and optional backend-gated workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

DeepChem Model Training

Use this sub-skill when the task is about constructing deepchem.models objects, calling fit(), evaluate(), predict(), selecting deepchem.metrics.Metric, running deepchem.hyper searches, saving/restoring models, or planning model families and optional backends.

Route adjacent work elsewhere:

• Data loading, MoleculeNet loaders, dataset transforms, and splits: ../data-and-molnet/.
• Molecular featurizers and feature/model compatibility: ../featurization/.
• Docking or structure scoring workflows: ../docking-and-structure/.

Start with a base-dependency baseline unless the user explicitly requests neural models:

1. Confirm the dataset is already a DeepChem Dataset with compatible X, y, and w shapes.
2. Pick a model family from references/model-families.md; prefer SklearnModel for fast baselines in minimal installs.
3. Pick metrics from references/api-reference.md; match classification metrics to probability/class-label shape requirements.
4. Fit, evaluate, predict, and persist using references/training-workflows.md.
5. If a backend warning appears or an import fails, consult references/troubleshooting.md before installing extras.

For a deterministic smoke test that avoids TensorFlow/PyTorch/JAX, run:

python sub-skills/model-training/scripts/train_tiny_sklearn_model.py

Expected output includes a JSON object with score_key, prediction_shape, and restored_prediction_shape.