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magine that you work for a university that wants to use machine learning and Naive Bayes to predict which students might have difficulty graduating. So you create three predictors. These are financial hardship, grade point average and class attendance. In a meeting, a data scientist points out that you might not want to use class attendance and grade point average because they are strongly autocorrelated. If someone doesn't attend class, then they $1$ ll likely get a poor grade. How might you answer this question?

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Naive Bayes is naive because it can classify even when predictors are autocorrelated.

Class attendance and grade point average are not closely related.

It might be a good idea to change the predictors so that they're not autocorrelated.

Naive Bayes is naive because it doesn't need class predictors to classify the data.

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