Data
forest_fires

forest_fires

active ARFF Public Domain (CC0) Visibility: public Uploaded 19-04-2020 by Rafael Gomes Mantovani
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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. Attribute Information: 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).

13 features

area (target)numeric251 unique values
0 missing
Xnumeric9 unique values
0 missing
Ynumeric7 unique values
0 missing
monthnominal12 unique values
0 missing
daynominal7 unique values
0 missing
FFMCnumeric106 unique values
0 missing
DMCnumeric215 unique values
0 missing
DCnumeric219 unique values
0 missing
ISInumeric119 unique values
0 missing
tempnumeric192 unique values
0 missing
RHnumeric75 unique values
0 missing
windnumeric21 unique values
0 missing
rainnumeric7 unique values
0 missing

19 properties

517
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
11
Number of numeric attributes.
2
Number of nominal attributes.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
-13.81
Average class difference between consecutive instances.
84.62
Percentage of numeric attributes.
0.03
Number of attributes divided by the number of instances.
15.38
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.

9 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: area
0 runs - estimation_procedure: 33% Holdout set - target_feature: area
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task