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name: multitask-modeling description: "Use DeepCTR-Torch SharedBottom, ESMM, MMOE, and PLE multi-task models with aligned labels, losses, metrics, and predictions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

multitask-modeling

Use this sub-skill when the task needs one DeepCTR-Torch model to optimize more than one target with the implemented multi-task classes: SharedBottom, ESMM, MMOE, or PLE.

Route map

• Build SparseFeat, DenseFeat, VarLenSparseFeat, feature names, and model_input dictionaries with ../feature-column-inputs/SKILL.md first.
• Use references/mtl-models-and-training.md for model selection, label matrix construction, compile/fit/predict flow, ByteRec-style two-target recipes, and metric handling.
• Use references/api-reference.md for exact constructor signatures, supported task types, loss strings, output shape, and parameter notes.
• Use references/troubleshooting.md before changing model code when target shape, task order, loss list, ESMM semantics, flattened metrics, or tiny batch behavior looks wrong.
• Run scripts/mmoe_multitask_smoke.py --help or the default smoke command to verify that a local installation can train an MMOE on inline two-label data and return predictions shaped (n_samples, 2).
• Route single-output CTR/ranking models, single-target target arrays, persistence, and callbacks to ../single-task-modeling/SKILL.md and shared training references.

Operating rules

1. Multi-task labels are a two-dimensional NumPy-like matrix shaped (n_samples, num_tasks). Column i must be the label for task_names[i].
2. model.predict(model_input, batch_size) returns a NumPy array shaped (n_samples, num_tasks). Prediction column i belongs to task_names[i]; evaluate each task column explicitly.
3. task_types length must equal len(task_names). Supported values are "binary" and "regression" for SharedBottom, MMOE, and PLE; ESMM accepts two binary tasks only.
4. Compile multi-task models with a loss list of the same length and order as task_names, for example ['binary_crossentropy', 'binary_crossentropy'] or ['binary_crossentropy', 'mse'].
5. Do not claim support for arbitrary custom task graphs: this package exposes the four fixed multi-task architectures above.