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Answer: One-hot encoding creates binary variables for each category, which can lead to high dimensionality and sparse data.
One-hot encoding involves creating binary variables for each category of a categorical feature, which can lead to high dimensionality and sparse data, especially for high-cardinality features. This can be inefficient and can complicate the model-building process. An alternative encoding method that can be more efficient for high-cardinality features is feature hashing or binary encoding, which reduces the dimensionality by mapping categories to a fixed-size vector or binary code, respectively.
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Explain the concept of one-hot encoding and its limitations in the context of high-cardinality categorical features. Discuss an alternative encoding method that can be more efficient for such features.
A
One-hot encoding creates binary variables for each category, which can lead to high dimensionality and sparse data.
B
One-hot encoding is efficient for all types of categorical features, including high-cardinality features.
C
One-hot encoding should not be used for categorical features.
D
One-hot encoding is only suitable for low-cardinality categorical features.
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