Please help with the following mathematical and statistical problems.

1. Evaluate the Nave Bayes Classifier and compare it against the other methods presented such as logistic regression, nonlinear discriminant analysis, classification tree, penalized model, and neural network? Why did the Nave Bayes model outperform all the other models except for mixture discriminant and classification tree? How did the sensitivity of the model factor into the model validation? [Reference: Read Kern et al. (2017) ]
2. Explain how the Nave Bayes Classifier applies to the Internet of Things. How does the max(P(T|Ci)) of the Gaussian probability function help in evaluating an image texture derived from malware analysis? [Reference: Read Karanja et al. (2020).]