How participant-aware evaluation changed performance across three machine-learning models The post When EEG Models See the Same People in Training and Testing appeared first on Towards Data Science.
How participant-aware evaluation changed performance across three machine-learning models The post When EEG Models See the Same People in Training and Testing appeared first on Towards Data Science.
In context
- Topic: Datos y Analítica — Gobierno del dato, calidad, analítica y productos de datos.
- Source: Towards Data Science
- Published: 17/10/2026
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Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.
Why it matters
In data, the problem is almost never the tool. It is that two areas mean different things by customer and neither wants to give ground. You can install the most modern platform on the market and still have three sales figures in the same meeting.
My criterion is that data only helps if it arrives in time for the decision it is meant to support. A perfect report that lands on the eighth for a decision taken on the third is a useless report, however correct it is. Timeliness beats precision more often than people admit.
What usually goes wrong
What I see fail most is dashboard maintenance. It gets built carefully, used for three months, and then a business rule changes that nobody told the data team about. The number still comes out, it no longer means the same thing, and decisions keep being made on data that became false.
What to watch
- Where the data comes from and what happens when the source system changes or fails.
- When the number arrives relative to when the decision is taken, which is what defines its usefulness.
- Who answers for the quality of the indicator when somebody challenges it in a committee.
How I read this entry
If this reached an organisation, I would start by agreeing ten definitions rather than a hundred: what an active customer is, what a sale is, when it counts. Written, signed and published. It is a week of uncomfortable argument that saves two years of meetings where nobody agrees.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
Comments
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
