The Iris Cassification Based on Gaussian Naive Bayes Agorithm
YuXuan Shi, Hongli Xu · 2022
Bayes' theorem is one of the most famous theories in probabilistic models, which is adept to be combined in machine learning, especially in classification applications. IRIS set is the classical dataset of machine learning. Firstly, naive and Gaussian Bayes classifier are provided including their relationship. Secondly, preprocess IRIS dataset, use CSV Format or read dataset into a Pandans DataFrame, perform prior, divide them into training and test datasets in proportion. And then, perform train and test work according to corresponding data with Gaussian Bayes classifier. Finally, carry out cross validation and analyze the accuracy of the verification results. The emphasis is on the working of naive Bayes model with Gause principle and the improvement on the method of dataset feature extraction.