Exam Professional Machine Learning Engineer topic 1 question 78 discussion

B) Hash buckets

went with A

Went with B

https://cloud.google.com/ai-platform/training/docs/algorithms/wide-and-deep If the column is categorical with high cardinality, then the column is treated with hashing, where the number of hash buckets equals to the square root of the number of unique values in the column.

B. The other options solves nada.

https://towardsdatascience.com/getting-deeper-into-categorical-encodings-for-machine-learning-2312acd347c8 When you have millions uniques values try to do: Hash Encoding

B unconditoinally https://cloud.google.com/ai-platform/training/docs/algorithms/xgboost#analysis If the column is categorical with high cardinality, then the column is treated with hashing, where the number of hash buckets equals to the square root of the number of unique values in the column. A categorical column is considered to have high cardinality if the number of unique values is greater than the square root of the number of rows in the dataset.

I think C as it has 10000 categorical values

I think B is correct Ref.:" - https://cloud.google.com/ai-platform/training/docs/algorithms/xgboost - https://stackoverflow.com/questions/26473233/in-preprocessing-data-with-high-cardinality-do-you-hash-first-or-one-hot-encode

Answer is B. When cardinality of the categorical column is very large best choice is binary encoding however it not here hence one-hot hash option.

Ans : B

B is correct

It should be B

Answer A since with 10.000 unique values one-hot shouldn't be a good solution https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/