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name: supervised-models description: "Choose, configure, train, evaluate, and troubleshoot River supervised classification and regression models." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

River supervised models

Use this sub-skill when the task is to choose or configure River classifiers, regressors, supervised ensembles, supervised wrappers, optimizers, losses, or supervised model-selection adapters.

Load order

1. Read references/model-family-guide.md to pick a model family and identify constraints such as sparse features, non-linear splits, memory budgets, multiclass targets, or interaction effects.
2. Read references/optimizers-and-losses.md before changing linear_model or facto optimizers, losses, learning-rate schedules, initializers, regularization, or sample weights.
3. Read references/wrappers-and-compat.md when adapting binary models to multiclass, modeling multioutput targets, wrapping River models for scikit-learn, wrapping scikit-learn incremental estimators for River, or using model selection.
4. Read references/troubleshooting.md whenever predictions, metrics, targets, optional dependencies, sample weights, or tree/forest memory behavior are surprising.
5. Use scripts/supervised_model_smoke.py for a tiny offline sanity check of representative supervised workflows.

Route elsewhere

• Generic estimator contracts, method semantics, tags, cloning, and custom estimator checks belong to online-core-api.
• Stream iteration, progressive validation, metrics mechanics, delayed labels, and benchmark loops belong to streaming-evaluation.
• Pipelines, feature extraction, preprocessing, scaling, unions, and feature routing belong to pipelines-and-features.
• Drift detectors as a standalone workflow, anomaly detection, clustering, forecasting, bandits, recommender systems, imbalanced-learning utilities, and probability distributions belong to specialized-workflows.

Fast routing cues

• Use linear_model for scalable sparse/dense dictionary classification or regression where scaling and optimizer control are acceptable.
• Use tree or forest for non-linear supervised streams, mixed numeric/categorical dictionaries, and models that should adapt by incremental splits rather than by manually engineered interactions.
• Use ensemble when several online models should vote, bag, boost, stack, hedge, or random-patch the stream.
• Use naive_bayes for small probabilistic baselines, text/count dictionaries, or quick multiclass classification.
• Use neighbors when a bounded sliding window of recent examples is the right inductive bias.
• Use facto for sparse high-cardinality dictionaries where pairwise or higher-order interactions matter.
• Use multiclass, multioutput, model_selection, and compat only after confirming the base model, target shape, metric, and optional dependency requirements.