---
name: Choose Covariance Estimator
slug: choose-covariance-estimator
category: AI Engineering
description: Choose Covariance Estimator picks a covariance estimator for a 2-D dataset using sample features like dimension, conditioning, and tail behavior. Use it when you need a shortlist of estimators or a safe default for ill-conditioned or heavy-tailed data.
github: "https://github.com/microprediction/precise/tree/main/.claude/skills/choose-covariance-estimator"
language: Python
stars: 333
forks: 58
install: "npx degit https://github.com/microprediction/precise/tree/main/.claude/skills/choose-covariance-estimator ~/.claude/skills/choose-covariance-estimator"
installs_to: ~/.claude/skills/choose-covariance-estimator
source_path: .claude/skills/choose-covariance-estimator/SKILL.md
collection_size: 6
category_size: 2451
collection_url: "https://dirskills.com/collections/microprediction/precise"
added: 2026-08-12T04:43:52.170Z
last_synced: 2026-08-12T04:43:52.170Z
canonical_url: "https://dirskills.com/skills/choose-covariance-estimator"
---

# Choose Covariance Estimator

Choose Covariance Estimator picks a covariance estimator for a 2-D dataset using sample features like dimension, conditioning, and tail behavior. Use it when you need a shortlist of estimators or a safe default for ill-conditioned or heavy-tailed data.

**Install:**

```bash
npx degit https://github.com/microprediction/precise/tree/main/.claude/skills/choose-covariance-estimator ~/.claude/skills/choose-covariance-estimator
```

## README

# Choose a covariance estimator for your data

No estimator wins everywhere, so `precise` recommends one from observable, truth-free features.

```python
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:

```python
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.