Feature engineering on forest cover type data with ensemble of decision trees

The paper aims to determine the forest cover type of the dataset containing cartographic attributes evaluated over four wilderness areas of Roosevelt National Forest of Northern Colorado. The cover type data is provided by US Forest service inventory, while Geographic Information System (GIS) was used to derive cartographic attributes like elevation, slope, soil type etc. Dataset was analyzed, pre processed and feature engineering techniques were applied to derive relevant and non-redundant features. A comparative study of various decision tree algorithms namely, CART, C4.5, C5.0 was performed on the dataset. With the new dataset built by applying feature engineering techniques, Random Forest and C5.0 improved the accuracy by 9% compared to the raw dataset.