Performance Analysis of Decision Tree Algorithms on Mushroom Dataset
B. Lavanya · International Journal for Research in Applied Science and Engineering Technology · 2017
Data mining is a process of extracting valuable information from vast dataset databases. This paper discusses data mining algorithms namely ID3, CART, and HoeffdingTree (HT) based on decision tree. Our goal is to assess the risk to human life through the consumption of mushrooms. Decision tree is used to classify the data with 22 attributes, to either edible or poisonous Mushrooms. Decision tree is used in this study as classification technique for analyzing mushroom data set. This experiment has been performed in R studio software environment. This study illustrates the accuracy of each classifier and the results are compared and discussed to which classifier is best for mushroom dataset. Hoeffding Tree provides better results with highest accuracy, low time and least error rate when compared with ID3 and CART.