A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques…

The correct answer is B. One-hot encoding D. Label encoding. One-hot encoding creates binary indicator columns for each category, allowing models to treat nominal categories without implying any order. Label encoding maps categories to integer labels, which can be useful for tree-based models or when you need a single numeric column…

Data Processing Technologies

Question

A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)

Options

ANormalization
BOne-hot encoding
CLinearization
DLabel encoding
EScaling
FPivoting

How the community answered

(31 responses)

B
94% (29)
E
3% (1)
F
3% (1)

Explanation

One-hot encoding creates binary indicator columns for each category, allowing models to treat nominal categories without implying any order. Label encoding maps categories to integer labels, which can be useful for tree-based models or when you need a single numeric column (though you must ensure the algorithm can handle treated ordinality appropriately).

Topics

#one-hot encoding#label encoding#categorical features#feature engineering

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