Working with Categorical Data: Test Your Knowledge Return to pathway Which of the following are examples of categorical data? (Choose all that apply) Choose as many answers as you see fit. 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" True or False: Machine labels are generally considered more desirable than labels provided by human raters. True False 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) Choose as many answers as you see fit. [0, 1, 0, 0, 0, 0] [1] [1, 2, 3, 4, 5, 6] [0, 1] [1, 0, 2, 3, 4, 5] 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. 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 Submit answers error_outline An error occurred when grading the quiz. Please try again.
