A healthcare dataset contains a column for 'Age' and another for 'Blood Pressure'. If you want to ensure both features contribute equally to the distance metric in a k-NN algorithm, what should you do?
- Standardize both 'Age' and 'Blood Pressure'
- Normalize both 'Age' and 'Blood Pressure'
- Use Euclidean distance as the metric
- Give more weight to 'Blood Pressure'
To ensure that both 'Age' and 'Blood Pressure' contribute equally to the distance metric in a k-NN algorithm, you should standardize both features. Standardization scales the features to have a mean of 0 and a standard deviation of 1, preventing one from dominating the distance calculation. Normalization may not achieve this balance, and changing the distance metric or giving more weight to one feature can bias the results.
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