Slot: Cross-Validated RMSE (cv_rmse)
Cross-validated RMSE for the model as a whole.
Why this slot matters
R² alone hides how large the errors actually are. RMSE states the typical error in the value's own units, which is what determines whether model error is negligible or fatal for a given effect size.
In plain terms
Root-mean-square error — the typical size of the model's mistakes, in the same units as the value itself (e.g. °C or µg/m³). Smaller is better.
URI: envar:slot/cv_rmse
Applicable Classes
| Name |
Description |
Modifies Slot |
| ModelAggregateUncertainty |
Whole-model uncertainty summary — cross-validation metrics and the reference ... |
no |
Properties
Type and Range
Cardinality and Requirements
| Property |
Value |
| ### Slot Characteristics |
|
Examples
Annotations
| property |
value |
| justification |
R² alone hides how large the errors actually are. RMSE states the typical error in the value's own units, which is what determines whether model error is negligible or fatal for a given effect size. |
| explanation |
Root-mean-square error — the typical size of the model's mistakes, in the same units as the value itself (e.g. °C or µg/m³). Smaller is better. |
Schema Source
Mappings
| Mapping Type |
Mapped Value |
| self |
envar:cv_rmse |
| native |
envar:cv_rmse |
LinkML Source
name: cv_rmse
annotations:
justification:
tag: justification
value: R² alone hides how large the errors actually are. RMSE states the typical
error in the value's own units, which is what determines whether model error
is negligible or fatal for a given effect size.
explanation:
tag: explanation
value: Root-mean-square error — the typical size of the model's mistakes, in the
same units as the value itself (e.g. °C or µg/m³). Smaller is better.
description: Cross-validated RMSE for the model as a whole.
title: Cross-Validated RMSE
examples:
- value: '1.89'
description: Di et al. ensemble PM2.5, in µg/m³ (same model as the cv_r2 example)
from_schema: https://w3id.org/linkml/microschemas/envar
rank: 1000
owner: ModelAggregateUncertainty
domain_of:
- ModelAggregateUncertainty
range: float