Edibility detection of mushroom using Logistic Regression and PCA

Sai Charan Gangu, Madhu Nitesh Bandi, Dr Sangeeta Viswanadham, Chintala Chandrasekhar Sivaji, Toyaka Sai Kiran

Abstract


Mushroom is found to be one of the best nutritional foods with high proteins, vitamins and minerals. Only some of the mushroom varieties were found to be edible. Some of them are dangerous to consume. To distinguish between the edible and poisonous mushrooms, we use machine learning algorithms to classify them. Classification is performed using various machine learning classifiers and Logistic regression showed better results compared to other algorithms. A survey of various algorithms resulted in KNN giving an accuracy of 100% at k=1 using 800 samples. A change k value is leading to a decrease in accuracy. By using hybrid algorithms (i.e., using two or more algorithms) which includes a combination of dimensionality reduction techniques such as Linear Discriminant Analysis(LDA) and Principal Component Analysis(PCA) along with existing classifiers better performance is achieved. Logistic Regression along with Principal Component Analysis is used to increase the accuracy. The results are shown in form of bar plots.

Keywords


Mushrooms, Poisonous, Classification, Logistic Regression, Linear Discriminant Analysis, Principal Component Analysis, Bar plot.

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References


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DOI: https://doi.org/10.26483/ijarcs.v13i3.6830

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