Working with Categorical Data: Test Your Knowledge

1. Which of the following are examples of categorical data? (Choose all that apply)

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   Number of pages in a book

   Telephone number

   Type of french fries (curly, crinkle-cut, steak-cut, waffle)

   Star rating (1 to 5 stars) for a restaurant, where 1 star indicates "poor" and 5 stars indicates "excellent"

2. True or False: Machine labels are generally considered more desirable than labels provided by human raters.

   True

   False

3. You are training a model on a training dataset that includes the feature eye_color, which can be one of the following six values: amber, blue, brown, gray, green, hazel.
   Which of the following are valid encodings for an eye_color value of blue? (Choose all that apply)

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   [0, 1, 0, 0, 0, 0]

   [1]

   [1, 2, 3, 4, 5, 6]

   [0, 1]

   [1, 0, 2, 3, 4, 5]

4. In which of the following scenarios would it make sense to apply feature hashing?

   The number of categorical feature values is very large.

   The number of categorical feature values is very small.

   The model is being trained offline.

   All the possible values of the categorical feature can be enumerated in advance.

5. You are performing a feature cross of the following two categorical features:

   • apple_color, which takes one of these four values: green, red, white, or yellow
   • apple_texture, which takes one of these two values: crisp or mushy

   How many entries are in the resulting feature-cross vector?

   1

   2

   6

   8

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