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name: prediction-algorithms description: "Choose, configure, fit, and extend Surprise prediction algorithms, including baselines, similarities, prediction details, neighbor retrieval, and custom AlgoBase subclasses." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Prediction Algorithms

Use this sub-skill when you need to choose a Surprise predictor, tune baseline or similarity options, inspect Prediction details, retrieve neighbors, or implement a custom AlgoBase subclass.

Route here

• Pick a built-in algorithm: NormalPredictor, BaselineOnly, KNNBasic, KNNWithMeans, KNNWithZScore, KNNBaseline, SVD, SVDpp, NMF, SlopeOne, or CoClustering.
• Configure bsl_options and sim_options for baseline-centric or neighborhood methods.
• Work with predict(), test(), Prediction, and PredictionImpossible.
• Call get_neighbors() on similarity-based algorithms and convert inner ids back to raw ids when you need to display them.
• Extend AlgoBase when the built-ins do not match the target behavior.

Stay out

• Dataset loading, reader formats, Trainset construction, and raw/inner id basics belong in the data-loading sub-skill.
• cross_validate, GridSearchCV, iterator strategy, and metric orchestration belong in evaluation-and-search.
• Top-N recommendation, precision/recall@k, and dump/load workflows belong in recommendation-and-analysis.

Start fast

1. Choose the family:
   • BaselineOnly if biases are the target.
   • KNN* if you want neighborhood aggregation and actual_k.
   • SVD, SVDpp, or NMF if you want latent factors.
   • SlopeOne or CoClustering if you want their specialized rules.
2. Decide whether the algorithm uses bsl_options, sim_options, both, or neither.
3. Fit on a Trainset, then inspect Prediction.details from predict() or test().
4. Pass inner ids to get_neighbors() and expect inner ids back.
5. For custom algorithms, subclass AlgoBase, call the base __init__ and fit, then implement estimate().

Smoke checks

• python scripts/predict_single_rating_smoke.py
• python scripts/baseline_similarity_smoke.py
• python scripts/get_neighbors_smoke.py
• python scripts/custom_algorithm_smoke.py

References

• references/algorithms.md covers algorithm families, constructor knobs, fit/predict/test semantics, Prediction details, and custom subclass patterns.
• references/troubleshooting.md maps common option, neighbor, randomness, and fallback failures to fixes.