Question

Imagine 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'll likely get a poor grade. How might you answer this question? Select an answer:

a. Naive Bayes is naive because it can classify even when predictors are autocorrelated.
b. Class attendance and grade point average are not closely related.
c. It might be a good idea to change the predictors so that they're not autocorrelated.
d. Naive Bayes is naive because it doesn't need class predictors to classify the data.

Answer 1

Naive Bayes can handle autocorrelated predictors due to its naive assumption of predictor independence, but choosing less correlated predictors might improve model quality. Financial hardship, class attendance, and GPA can still be used, with Naive Bayes acting under the assumption of independence.

Step-by-step explanation:

When considering whether to use class attendance and grade point average as predictors for a machine learning model using Naive Bayes, we must acknowledge the concern of autocorrelation between these two variables. Autocorrelation means that the predictors may not provide independent information to the model, as class attendance can influence the grade point average.

The best answer to the question would be option a: Naive Bayes is naive because it can classify even when predictors are autocorrelated. This property of Naive Bayes allows it to function under the assumption that each predictor contributes independently to the outcome, despite potential correlations in the real world.

It's important to note that while Naive Bayes can handle autocorrelated predictors, the quality and predictive power of the model may benefit from carefully choosing predictors that provide unique information about the outcome of interest. In the case of student graduation, other factors such as engagement in academic support services or financial aid reception could also be considered.