ILLUSTRATIVE · NASA / SVS · NOT ADITYA-L1 DATA
Model cardslogistic
Model card · candidate detector
Logistic regression
A linear model over the fourteen features, squashed to a probability.
What is this?
A model card for Logistic regression, one of eight detectors evaluated on the frozen Aditya-L1 flare benchmark.
Why should I trust it?
Every figure below is read at build time from the committed benchmark artifact; none is transcribed. The judgements are traceable to the written evaluation protocol and the dataset limitations record.
Where can I verify it?
- The validation evidenceComputed from 192,541 held-out predictions
- Dataset limitationsL-1 … L-10
- Evidence traceabilityclaim → artifact → commit
- The raw payloadfindings/models.json
Section 1
Purpose and intended use
Why it is in the benchmark. The lowest-capacity learned model, included to separate 'learning helps' from 'flexibility helps'. If a linear combination matches an ensemble, the extra capacity was buying nothing.
Intended use. Understanding which features carry the signal, via coefficients that can be read directly.
Out of scope. Its coefficients are correlational, not causal, and the features are strongly collinear — several rolling means measure nearly the same thing. A negative coefficient is not evidence that the quantity suppresses flares.
Section 2
Inputs, outputs, training
Inputs. Fourteen features per minute, all derived from the SoLEXS 1-minute count rate in T1: the log rate, rolling means over 5/15/30/60 minutes, a 15-minute rolling maximum, rolling standard deviations, a background-excess term, two rise rates, and three data-quality terms (GTI fraction, seconds present, partial-minute flag).
Outputs. A probability per minute.
Training protocol. Chronological split with a held-out test period beginning at the frozen test start date; no shuffling, so no future minute can inform a past one. The protocol — split, seed, metric set and bootstrap scheme — was fixed in EVALUATION_PROTOCOL.md before any model was fitted.
Operating point. Decision threshold 0.9524, selected on the training period and then frozen.
Section 3
Evaluation
Both tasks, both reported.
M/X NOWCAST · 192,541 held-out minutes · base rate 1.24%
| Metric | Value | What it measures |
|---|---|---|
| ROC-AUC | 0.964 | Ranking quality across all thresholds |
| PR-AUC | 0.556 | The informative curve at a low base rate |
| Precision | 0.540 | Of the minutes flagged, how many were flares |
| Recall | 0.537 | Of the flare minutes, how many were flagged |
| F1 | 0.539 | Harmonic mean of the two above |
| MCC | 0.533 | Correlation, robust to imbalance |
| Brier | 0.029 | Probability accuracy; undefined for hard classifiers |
| Balanced accuracy | 0.766 | Mean of the two class recalls |
- Events detected
- 80 / 82
- Event recall 0.976 · 95% CI 0.929–1.000
- False alarm runs
- 228
- Of 320 alarm runs raised. A run, not a minute — the unit an operator actually responds to.
- ROC-AUC interval
- 0.953–0.974
- 95%, day-block bootstrap.
M/X 30-MIN PREDICTION · 192,541 held-out minutes · base rate 1.29%
| Metric | Value | What it measures |
|---|---|---|
| ROC-AUC | 0.780 | Ranking quality across all thresholds |
| PR-AUC | 0.120 | The informative curve at a low base rate |
| Precision | 0.155 | Of the minutes flagged, how many were flares |
| Recall | 0.356 | Of the flare minutes, how many were flagged |
| F1 | 0.216 | Harmonic mean of the two above |
| MCC | 0.220 | Correlation, robust to imbalance |
| Brier | 0.060 | Probability accuracy; undefined for hard classifiers |
| Balanced accuracy | 0.665 | Mean of the two class recalls |
- Events detected
- 38 / 82
- Event recall 0.463 · 95% CI 0.268–0.600
- False alarm runs
- 221
- Of 254 alarm runs raised. A run, not a minute — the unit an operator actually responds to.
- ROC-AUC interval
- 0.674–0.850
- 95%, day-block bootstrap.
Confusion matrix · M/X NOWCAST
True negative
189,053
False positive
1,095
False negative
1,107
True positive
1,286
Section 4
Feature attribution
Which of the fourteen features the fit relied on.
Attribution is not explanation. These features are strongly collinear — four of them are rolling means of the same series — so credit assigned to one is credit withheld from another, and none of it constitutes a physical account of flare onset.
Section 5
Strengths and failure modes
Strengths
- Coefficients are directly readable, including their sign.
- Low capacity, which matters when the effective sample size is events rather than minutes.
Failure modes
- Collinear features split credit unstably between themselves.
- A linear decision surface cannot express 'high rate but flat and quiet', which is what separates a real rise from a noisy plateau.
Section 6
Limitations and ethical considerations
Ethical considerations. Readable coefficients invite over-interpretation. Presenting them as physical mechanism would be the misuse this card exists to pre-empt.
Governing limitation clauses
Clause text is not restated here. It is published in full on the dataset card, from the frozen DATASET_LIMITATIONS_FOR_ML.md report, so there is exactly one copy of every caveat.