Performance Comparison of PCA and LDA with Linear Regression and Random Forest for IRIS Flower Classification
Classification is the mostly used machine learning problem now a day for varieties of application use in the field of security, agriculture, industry etc. This paper performs the classification of IRIS flower using Logistic regression and random forest algorithm. Principal component analysis (PCA) and linear discriminant analysis (LDA) used as feature extraction method for both case. In the second part a comparative study has been propose between the performance of both the machine learning method as well as both the dimensionality reduction method. The comparative study reveals that the LDA work far better than PCA, where as using LDA the logistic regression and random forest method gives nearly same result.
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How to Cite
Debaraj Rana, Swarna Prabha Jena, Subrat Kumar Pradhan. (2020). Performance Comparison of PCA and LDA with Linear Regression and Random Forest for IRIS Flower Classification. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(9), 2353 - 2360. Retrieved from https://www.archives.palarch.nl/index.php/jae/article/view/4145