<div class="section-label"><span class="num">05</span> Relevance Metrics</div>
<div class="section-title">Score <em>relevance </em>without predicting.</div>
Sometimes you want the building blocks on their own: how similar each past case is to your circumstances, how informative those cases are, and how they combine into relevance, without forming a full forecast.
These scores are what Predict, MaxFit, and Grid use internally when they decide which observations enter the weighted average.
<p> </p>
## When to use Relevance Metrics
- You want relevance, similarity, or informativeness as analysis outputs
- You already have (or can omit) an inverse covariance and only need the score vectors
- You want several scores filled in one pass
> [!note]
> Need a full forecast? Prefer [[Grid Prediction]] for primary analysis. Use [[Predict]] or [[MaxFit]] when you need those building-block paths. The same scores can also appear on prediction results. See [[Results/Weights & Insights|Weights & Insights]].
## What you call
```text
relevance(X, theta, cov_inv?) → scores length N
similarity(X, theta, cov_inv?) → scores length N
info_x(X, cov_inv?) → scores length N # no theta
info_theta(X, theta, cov_inv?) → score length 1
relevance_metrics(X, theta, cov_inv?) →
optional: relevance, similarity, info_x, info_theta
```
<span class="mono">X</span> is N × K. <span class="mono">theta</span> has length K (except for <span class="mono">info_x</span>). Optional <span class="mono">cov_inv</span> is K × K; if you omit it, the engine estimates it from <span class="mono">X</span>.
With <span class="mono">relevance_metrics</span>, you can request only the scores you need.
## What the scores mean
| Score | Shape | Idea |
| --- | --- | --- |
| Similarity | N | How close each training row is to <span class="mono">theta</span> |
| Informativeness of X (<span class="mono">info_x</span>) | N | How unusual each row is vs the sample mean |
| Informativeness of θ (<span class="mono">info_theta</span>) | 1 | How unusual <span class="mono">theta</span> is vs the sample mean |
| Relevance | N | Combines similarity with informativeness of the row and of <span class="mono">theta</span> |
When you run Predict / MaxFit / Grid, censoring chooses **relevance** or **similarity** (or both on MaxFit/Grid) as the score that decides include vs exclude.
<div class="btn-row center">
<a class="btn-primary" href="/Functions/Grid%20Prediction">Prefer Grid Prediction</a>
<a class="btn-ghost" href="/Config/Options#PredictOptions">PredictOptions</a>
</div>
## Related
- [[Predict]]: uses these scores inside the forecast
- [[Config/Options#PredictOptions|PredictOptions]]: <span class="mono">censor_type</span> picks which score drives masking
- [[Results/Weights & Insights|Weights & Insights]]: scores stored on a prediction result