choose-covariance-estimator
Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
适合你，如果你需要为数据集选择协方差估计器但不确定哪个合适。
npx oh-my-skill add microprediction/precise/choose-covariance-estimatorcurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- microprediction/precise/choose-covariance-estimatornpx oh-my-skill verify microprediction/precise/choose-covariance-estimator怎么用
商店整理自技能原文 · 版本 d89c883 · 表述以原文为准装上后，Claude会分析你提供的数据集，根据其维度、条件数和尾部分布等特征，推荐最合适的协方差估计方法，并给出推荐理由和特征摘要。
当你有一组数据，且不确定该用哪种协方差估计器时，Claude会自动分析并给出建议。
技能原文 SKILL.md
Choose a covariance estimator for your data
No estimator wins everywhere, so precise recommends one from observable, truth-free features.
from precise import suggest, covariance_features suggest(X, top=3) # -> list of estimator CLASSES, best first covariance_features(X) # -> dict of the features behind the choice
X is 2-D (rows = observations, columns = variables). Then:
Est = suggest(X, top=1)[0] est = Est() est.fit(X) # or stream rows with partial_fit cov = est.covariance_
What it keys on
covariance_features(X) returns p, n, p_over_n, effective_rank, sphericity, condition_number, mean_abs_offdiag_corr, avg_excess_kurtosis — all computed from the sample, none requiring the unknown truth. Internally suggest uses a frozen, numpy-only decision tree; broadly it routes high p/n or ill-conditioned data to shrinkage/factor estimators and heavy-tailed data to robust ones.
Guardrail (important)
suggest is a heuristic trained on synthetic regimes. In leave-one-generative-family-out tests it does not reliably beat the single best fixed estimator on a wholly novel data-generating family — it generalizes across samples within familiar regimes, not to arbitrarily new structure. So:
- Treat its output as a strong shortlist, not an oracle.
- If the decision matters, verify the shortlist out-of-sample on your own data with the score-covariance-estimate skill, rather than trusting the recommendation blind.
- A well-conditioned shrinkage estimator (
LedoitWolfCovariance/OASCovariance) is a safe default when in doubt.