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+Citation Request:
+  This dataset is public available for research. The details are described in [Cortez and Morais, 2007].
+  Please include this citation if you plan to use this database:
+
+  P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data.
+  In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence,
+  Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December,
+  Guimaraes, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9.
+  Available at: http://www.dsi.uminho.pt/~pcortez/fires.pdf
+
+1. Title: Forest Fires
+
+2. Sources
+   Created by: Paulo Cortez and An�bal Morais (Univ. Minho) @ 2007
+
+3. Past Usage:
+
+   P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data.
+   In Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence,
+   December, 2007. (http://www.dsi.uminho.pt/~pcortez/fires.pdf)
+
+   In the above reference, 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.
+
+4. Relevant Information:
+
+   This is a very difficult regression task. It can be used to test regression methods. Also,
+   it could be used to test outlier detection methods, since it is not clear how many outliers
+   are there. Yet, the number of examples of fires with a large burned area is very small.
+
+5. Number of Instances: 517
+
+6. Number of Attributes: 12 + output attribute
+
+   Note: several of the attributes may be correlated, thus it makes sense to apply some sort of
+   feature selection.
+
+7. 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).
+
+8. Missing Attribute Values: None