Why explainable features beat black-box scores

It is tempting to ship a single magic number. But utility engineers are trained to question inputs, and a score they cannot trace is a score they will not act on.

Auditable by design

Every climagrid feature maps back to a documented standard and a public data source. That makes the output something an engineer can check against their own field experience rather than take on faith.

Better inputs for machine learning

Clean, explainable features are also the right foundation for the predictive models utilities will build as they gather historical failure data. Good ML starts with good features, not opaque scores.

• Each feature is traceable to a standard and a public source.
• Transparent math invites review instead of blind trust.
• Explainable features serve as high-quality ML inputs later.