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Answer: Feature Scaling
The correct technique is Feature Scaling, which is supported by Databricks MLlib. This method standardizes numerical features to have a mean of 0 and a standard deviation of 1, ensuring that all features contribute equally to the model's performance. Techniques like Z-score normalization are commonly used for this purpose, helping to eliminate biases caused by differing scales and improving the model's training and effectiveness.
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In a machine learning project with numerical features of varying scales, which Databricks MLlib technique is used to standardize these features to a mean of 0 and a standard deviation of 1?
A
Outlier Detection
B
Feature Encoding
C
Data Imputation
D
Feature Scaling
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