THE RESULT
What the data shows
The largest computed metric is 6.548 for Extra trees; the smallest is 6.047 for Median baseline. Metric: mae (Hectares MAE).
This models severity conditional on a recorded observation, not fire occurrence. Log predictions can understate rare large areas; compare RMSE as well as MAE.
| model | mae | rmse | r2 | bias |
|---|---|---|---|---|
| Median baseline | 6.047 | 21.26 | -0.06553 | -5.272 |
| Ridge | 6.381 | 20.93 | -0.03325 | -3.721 |
| Extra trees | 6.548 | 20.84 | -0.02421 | -2.793 |
THE METHOD
From source to answer
Grouped-input regression on log1p area, inverse-transformed before original-unit scoring.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
Fixed 80/20 split of unique input groups (seed 42); exact input duplicates stay together.
Fixed configurations specified before scoring; no tuning on the holdout.
Target and input features
Target: area
Inputs: x, y, month, day, ffmc, dmc, dc, isi, temp, rh, wind, rain
THE NEXT DECISION
What follows from the finding
Use disappointing as well as successful benchmarks to define what additional event data are needed.
Where the conclusion stops
The sample consists of recorded fire observations, not all places and times at risk. Zero recorded area is not evidence of no ignition. No precise dates or event identifiers are supplied. This is not an emergency forecasting system.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.