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).
In iteration Take2, we plan to examine the feature selection technique of attribute importance ranking by using the Gradient Boosting algorithm. By selecting the most important attributes, we hope to decrease the modeling time and still maintain a similar level of accuracy when compared to the baseline model.
ANALYSIS: From iteration Take1, 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.
From the current iteration, the performance of the machine learning algorithms achieved an average accuracy of 77.84%. Extra Trees achieved an accuracy metric of 85.80% with the training dataset and processed the testing dataset with an accuracy of 86.19%, which was even better than the predictions from the training data. At the importance level of 99%, the attribute importance technique eliminated 23 of 54 total attributes. The remaining 31 attributes produced a model that achieved a comparable accuracy compared to the baseline model. The modeling time went from 2 minutes 2 seconds down to 1 minute 41 seconds, a reduction of 17.2%.
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: https://archive.ics.uci.edu/ml/datasets/Covertype
One source of potential performance benchmarks: https://www.kaggle.com/c/forest-cover-type-prediction/overview
The HTML formatted report can be found here on GitHub.
Daines Analytics 