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name: query-strategies description: "Router for modAL uncertainty, disagreement, batch, density, expected-error, multilabel, and strategy-combinator workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

query-strategies

Use this sub-skill when the learner or committee already exists and you need to choose, compose, or troubleshoot how it queries the pool. It covers uncertainty, disagreement, ranked batch, expected-error reduction, information density, multilabel SVM selectors, and selector/combinator helpers.

Query callables receive (learner_or_estimator, X_pool, **kwargs) and may return bare indices, an index/instance pair, or (indices, metrics). BaseLearner.query(..., return_metrics=True) only forwards metrics when the strategy supplies them.

Read

• references/strategy-reference.md — built-in strategies, signatures, return shapes, defaults, and selector behavior.
• references/custom-strategies.md — combined utilities, custom selectors, ranked batch helpers, density-aware strategies, and multilabel recipes.
• references/troubleshooting.md — NotFittedError, missing predict_proba, tuple-vs-index confusion, batch cost, and multilabel shape recovery.

Run

• scripts/query_strategy_smoke.py — deterministic tiny-data smoke for custom combinations, ranked batch returns, information density, and multilabel return shapes.

Route elsewhere

• ../learners-and-committees/SKILL.md — build ActiveLearner, Committee, or CommitteeRegressor objects before picking a strategy.
• ../bayesian-optimization/SKILL.md — Bayesian acquisition functions such as max_PI, max_EI, and max_UCB.
• ../deep-and-optional-integrations/SKILL.md — MC dropout and other optional deep-learning strategies.

Covered surfaces

• uncertainty: classifier_uncertainty, classifier_margin, classifier_entropy, uncertainty_sampling, margin_sampling, entropy_sampling
• disagreement: vote_entropy, consensus_entropy, KL_max_disagreement, vote_entropy_sampling, consensus_entropy_sampling, max_disagreement_sampling, max_std_sampling
• batch: uncertainty_batch_sampling, ranked_batch, select_instance, select_cold_start_instance
• expected error: expected_error_reduction
• density: information_density, similarize_distance
• multilabel: SVM_binary_minimum, max_loss, mean_max_loss, min_confidence, avg_confidence, max_score, avg_score
• utilities: multi_argmax, multi_argmin, shuffled_argmax, shuffled_argmin, weighted_random, make_linear_combination, make_product, make_query_strategy

Use when

• the query policy changes but the learning object stays the same
• you need to combine or normalize several utility measures
• you need to debug whether a strategy returns indices only or (indices, metrics)

Do not use when

• you still need learner or committee construction
• the task is Bayesian optimization
• the task depends on MC dropout or another deep-only acquisition path