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Answer: It increases the dimensionality of the dataset, potentially causing increased computational complexity and issues with model performance, such as overfitting.
The main disadvantage of using one-hot encoding for high cardinality categorical variables is that it increases the dimensionality of the dataset, which can lead to increased computational complexity and potential issues with model performance, such as overfitting.
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What is the primary drawback of using one-hot encoding for high cardinality categorical variables in a machine learning model?
A
One-hot encoding always leads to underfitting.
B
It decreases the dimensionality of the dataset.
C
One-hot encoding has no impact on computational complexity.
D
It increases the dimensionality of the dataset, potentially causing increased computational complexity and issues with model performance, such as overfitting.
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