You want to build a classification model to identify spam comments on a blog. You decide to use
the words in the comment text as inputs to your model. Which criteria should you use when
deciding which words to use as features in order to contribute to making the correct classification
decision?

Given the following sample of numbers from a distribution:
1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89
What are the five numbers that summarize this distribution (the five number summary of sample
percentiles)?

Given the following sample of numbers from a distribution:
1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89
What are two benefits of using the five-number summary of sample percentiles to summarize a
data set?

Given the following sample of numbers from a distribution:
1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89
How do high-level languages like Apache Hive and Apache Pig efficiently calculate approximately
percentiles for a distribution?

What is the best way to determine the learning rate parameters for stochastic gradient descent
when the distribution of the input data shifts over time?

Which two machine learning algorithm should you consider as likely to benefit from discretizing
continuous features?

You’ve built a model that has ten different variables with complicated independence relationships
between them, and both continuous and discrete variables that have complicated, multi-parameter
distributions.
Computing the joint probability distribution is complex, but it turns out that computing the
conditional probabilities for the variables is easy. What is the most computationally efficient for

computing the expected value?

What is one limitation encountered by all systems that employ collaborative filtering and use
preferences as input. In order to output product recommendations to consumers?

Why is the naive Bayes classifier “naive”?

Which three metrics are useful in measuring the accuracy and quality of a recommender system?