Forest Fires Data Set
This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data.
Data Set Information:
In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function.
Then, several Data Mining methods were applied. After fitting the models, the outputs were
post-processed with the inverse of the ln(x+1) transform. Four different input setups were
used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two
regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed
with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value:
12.71 +- 0.01 (mean and confidence interval within 95% using a t-student distribution). The
best RMSE was attained by the naive mean predictor. An analysis to the regression error curve
(REC) shows that the SVM model predicts more examples within a lower admitted error. In effect,
the SVM model predicts better small fires, which are the majority.
For more information, read [Cortez and Morais, 2007].
1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9
2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9
3. month - month of the year: 'jan' to 'dec'
4. day - day of the week: 'mon' to 'sun'
5. FFMC - FFMC index from the FWI system: 18.7 to 96.20
6. DMC - DMC index from the FWI system: 1.1 to 291.3
7. DC - DC index from the FWI system: 7.9 to 860.6
8. ISI - ISI index from the FWI system: 0.0 to 56.10
9. temp - temperature in Celsius degrees: 2.2 to 33.30
10. RH - relative humidity in %: 15.0 to 100
11. wind - wind speed in km/h: 0.40 to 9.40
12. rain - outside rain in mm/m2 : 0.0 to 6.4
13. area - the burned area of the forest (in ha): 0.00 to 1090.84
(this output variable is very skewed towards 0.0, thus it may make
sense to model with the logarithm transform).