We just use for learning … Ann Arbor, Michigan: Morgan Kaufmann. We usually use random forest if a tree is not enough. Duch W, Adamczak R, Grabczewski K (1996) Extraction of logical rules from training data using … To solve this problem, we designed a machine learning algorithm to classify oyster mushroom … Currently, there are three comparisons of the best classification algorithms in data mining, namely: Decision Tree (C4.5), NaïveBayes and Support Vector Machine … Mushroom_Classification This project aims at developing a machine-learning algorithm that will determine if a certain mushroom is edible or poisonous by its specifications like cap shape, cap color, gill color, etc. In this case, as we have perfect prediction using a single tree, it is not really necessary to use a Random Forest algorithm. In Proceedings of the 5th International Conference on Machine Learning, 73-79. using … Contaminated spawns must be classified and discarded before they are delivered to the fruiting stage. Classification of Mushroom Fungi Using Machine Learning Techniques October 2019 International Journal of Advanced Trends in Computer Science and Engineering 8(5):2378-2385 Classification process of poisonous mushroom or not will be easily conducted by learning machine using mining data as one of the ways to extract computer assisted knowledge. In most mushroom farms, humans visually classify spawn, which is labor-intensive and susceptible to human errors. 16.3.3.2 Use of Random Forest.