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Naïve Bayes Review Assignment

*1.*What assumptions are made by a naive Bayes classifier?*2.*How are Naive Bayes classifiers trained?*3.*What's the difference between multilabel and multinomial text classification?*4.*What is a prior probability?*5.*Construct a bag-of-words feature matrix for the following email: "Hi, Dan. Are we still on for Tuesday? We can meet for coffee or we can meet at my office. Best, Alec"*6.*What is a "bag-of-words"?*7.*Consider a Naive Bayes model with the parameters provided in Figure A. How would this model classify the following sentence: "This morning was freezing cold."*8.*What is the joint probability of a given individual with one lottery ticket in Cleveland, OH winning the grand prize on a clear day in the winter?*9.*How can a bag of words handle seeing words in testing that were not seen during training?*10.*What is the sigmoid function and why is it useful?*11.*When training a logistic regression model, a training example yields \( \sigma (w·x + b) = 0.73 \), though the actual value is \( y = 0 \). Using the cross-entropy loss function, what is the loss?

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