Analysis of Performance of Classification Algorithms in Mushroom Poisonous Detection using Confusion Matrix Analysis
John Heland Jasper C. Ortega
Abstract
Mushroom possesses potential benefits in terms of antioxidants, which are essential to the body. There are various health benefits one can get on consuming mushrooms, but not all types are said to be editable. There are types of mushrooms that are poisonous. The research aims to classify which among the mushroom are edible and poisonous based on given attributes such as odor, shape, texture, and color. The data set is originally donated from the Machine Learning Repository Department of the University of California in Irvine. This research focuses on analyzing the performance of the classification algorithms in mushroom poisonous detection. The researchers utilized the Knowledge Discovery in Databases processes in performing pattern extraction and performance analysis of algorithms. The decision tree method that is based on the entropy and information gain calculations has the highest computed accuracy result compare to other classification algorithms.