Which of the following datasets would Naive Bayes likely perform poorly on?

Naive Bayes is based on the assumption of independence among predictors. This means it presumes that the features used to predict the outcome are conditionally independent given the value of the class variable. When the features are highly correlated, this assumption is violated. As a result, the model can struggle to make accurate predictions because it may over-represent certain relationships that exist between the correlated features, ultimately leading to less reliable classifications.

In contrast, Naive Bayes can work well with text data, as it often involves independent features (like words in a document) contributing to the class probabilities. It can also handle image data effectively when there are many features, particularly in cases where certain features can be treated as independent observations. Similarly, it can perform adequately on categorical data with fewer categories, as long as the independence assumption holds. However, when faced with datasets that contain highly correlated features, the performance can degrade significantly due to the misguided reliance on independence, leading to inaccurate predictions.