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Answer: import category_encoders as ce encoder = ce.BinaryEncoder(handle_unknown='ignore') encoder.fit(df[['Country']]) df['Country'] = encoder.transform(df[['Country']])
The correct code snippet to perform binary encoding on the 'Country' feature while handling missing values is 'import category_encoders as ce encoder = ce.BinaryEncoder(handle_unknown='ignore') encoder.fit(df[['Country']]) df['Country'] = encoder.transform(df[['Country']])'. The 'handle_unknown='ignore'' parameter ensures that missing values are ignored during the encoding process, allowing the model to handle them separately.
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Consider a dataset with a categorical feature 'Country' having values 'USA', 'Canada', 'Mexico', and missing values. Write a code snippet to perform binary encoding on this feature using Python and the category_encoders library. Explain how this process handles missing values.
A
import category_encoders as ce encoder = ce.BinaryEncoder(handle_unknown='ignore') encoder.fit(df[['Country']]) df['Country'] = encoder.transform(df[['Country']])
B
import category_encoders as ce encoder = ce.BinaryEncoder() encoder.fit(df[['Country']]) df['Country'] = encoder.transform(df[['Country']])
C
import category_encoders as ce encoder = ce.BinaryEncoder(handle_unknown='error') encoder.fit(df[['Country']]) df['Country'] = encoder.transform(df[['Country']])
D
import category_encoders as ce encoder = ce.BinaryEncoder(handle_unknown='use_encoded_value', unknown_value=-1) encoder.fit(df[['Country']]) df['Country'] = encoder.transform(df[['Country']])
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