> For the complete documentation index, see [llms.txt](https://docs.everesteer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.everesteer.ai/scoring/ncorr.md).

# NCORR

NCORR is correlation after neutralizing your predictions against a **frozen core feature set**. The point is that a score should not come purely from a feature the platform already knows about.

The core set is frozen and train-selected. The same reference is used everywhere NCORR is reported: the Himalayas round score, the validation-diagnostics panel, and training-time checks. A displayed NCORR and a paid NCORR never come from different features.

{% stepper %}
{% step %}

### Rank-gaussianize

The same rank transform CORR uses on predictions. The kernel does not neutralize raw predictions.
{% endstep %}

{% step %}

### Build the operator

From the core columns **present on the scored frame**. While those columns are well-conditioned, the operator is the exact least-squares projection. Once they are not, it becomes a spectrally-anchored ridge. A homemade exact-OLS residual will not always match.
{% endstep %}

{% step %}

### Subtract

Subtract that projection from your transformed predictions.
{% endstep %}

{% step %}

### CORR again

Feed the residual through the CORR kernel (rank-gaussianize and power, then Pearson against the target). The rank transform is not applied once and left alone.
{% endstep %}
{% endstepper %}

{% hint style="info" %}
NCORR is **null** when none of the core features are present on the scored frame. The term is skipped rather than recomputed over a different feature space. A null NCORR means that entry has no round score, not a zero.
{% endhint %}


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