Multi-algorithm comparison and performance evaluation

This report selects the rice dataset from UCL to compare the performance of several classic classification algorithms in the rice classification task, including linear discriminant analysis, logistic regression, K-nearest neighbor KNN classification, and naive Bayes classification. Through data preprocessing and feature engineering, we run naive Bayes classifiers under different prior distributions and analyze the classification results in detail. In addition, we select evaluation criteria such as accuracy, precision, recall, and F1 score to compare and discuss the effectiveness of each classification algorithm. The final results show that the choice of different prior distributions also has a certain impact on the classification results, and the classification effects of linear discriminant analysis, logistic regression, and Gaussian Bayes are better. This article details the experimental process and results analysis, providing some reference value for how to classify rice.