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Answer: Selects one option from a provided list or tuple
The `hp.choice(label, options)` function in Hyperopt is designed to select one option from a given list or tuple, making it essential for defining categorical hyperparameters during the optimization process. This allows for the exploration of different configurations in machine learning models. The other options describe different Hyperopt functions: `hp.randint(label, upper)` for random integers, `hp.uniform(label, low, high)` for uniform distribution between two values, and `hp.normal(label, mu, sigma)` for values following a normal distribution.
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What is the function of hp.choice(label, options) in Hyperopt?
A
Generates a random integer within a specified range [0, upper)
B
Selects one option from a provided list or tuple
C
Produces a value uniformly distributed between low and high
D
Returns a real value following a normal distribution with mean mu and standard deviation sigma