Insight Into Predictive Models: On The Joint Use Of Clustering And Classification By Association (CBA) On Building Time Series
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data-driven, black box machine learning models have received a lot of attention in the field of building control. They have been used successfully to predict building behaviour given information like weather forecasts and real time sensor information. In these models, the occupant behaviour is considered to act exogenously on the building. We consider the users as active elements of the building operation control loop. To make educated control decisions they have to be informed about how the building will behave. Therefore, we propose a prediction model which explains to occupants the dayahead building behaviour using a clustering and classification by association model. We benchmark this approach to a neural network regression model and only observed a small loss of accuracy. Knowing the upcoming building behaviour, occupants can adjust their behaviour (e.g. putting on clothes) or the building systems settings (e.g. set points) accordingly. The proposed method is a promising way to decode complex regression models into readable rules, which in future may be useful in conjunction with for example voice-based virtual assistants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it