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Answer: One-Hot Encoding
One-Hot Encoding is the technique supported by Databricks MLlib for converting categorical variables into numerical representations. This method involves creating binary (0 or 1) indicators for each unique category within the variable, effectively transforming the categorical data into a format that can be provided to machine learning algorithms without implying any ordinal relationship between the categories. One-Hot Encoding is widely utilized in machine learning preprocessing steps to handle categorical data efficiently.
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Your team is working on converting categorical variables into numerical representations for machine learning models. Which of the following techniques, supported by Databricks MLlib, can facilitate this conversion?
A
Principal Component Analysis
B
Feature Scaling
C
Label Encoding
D
One-Hot Encoding
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