Multi-Classifying 47 the Gadget's Defect Is Determined by Comparing Characteristics Using Random Forest and Naive Bayes

This study presents an automated inspection method for Printed Circuit Boards that is capable of identifying the type and location of defects. This categorization methodology employs Naive Bayes and Random Forests. To better anticipate the risks associated with nutrients and medications, we contrasted Random Forest with Naive Bayes. At 0.8 G statistical power and 95 % confidence, we look at each of the 2000 sample sets ten times. The training dataset for the Kaggle recognition problem has 1035 tuples. The new Naive Bayess method is 69.47 % accurate, down from 97.32 % with Random Forest.Compared to Random Forest, Naive Bayes performs better. The significance level was 0.932, below 0.05 By identifying defect concentrations, Random Forest, which is more precise than Naive Bayes, improves inspection performance. By subjecting the proposed methodology to PCB data testing, we ascertained its efficacy. Random Forest, Naive Bayes, Printed Circuit Board, Circuit, Technology, Defect, Feature, Problem