Multi-Class Classification Model for Forest Cover Type Using Python Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Forest Cover Type dataset is a multi-class classification situation where we are trying to predict one of the seven possible outcomes.

INTRODUCTION: This experiment tries to predict forest cover type from cartographic variables only. This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices.

The actual forest cover type for a given observation (30 x 30 meter cell) was determined from the US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from the US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types).

ANALYSIS: The baseline performance of the machine learning algorithms achieved an average accuracy of 78.04%. Two algorithms (Bagged Decision Trees and Extra Trees) achieved the top accuracy metrics after the first round of modeling. After a series of tuning trials, Extra Trees turned in the top overall result and achieved an accuracy metric of 85.80%. By using the optimized parameters, the Extra Trees algorithm processed the testing dataset with an accuracy of 86.50%, which was even better than the predictions from the training data.

CONCLUSION: For this iteration, the Bagged Decision Trees algorithm achieved the best overall results using the training and testing datasets. For this dataset, Extra Trees should be considered for further modeling.

Dataset Used: Cover Type Data Set

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.