Given a machine learning algorithm that is highly sensitive to the range of input values, which scaling technique should you implement?
- Min-Max scaling because it scales all values between 0 and 1
- No scaling, as the original data values should be maintained
- Robust scaling because it is not affected by outliers
- Z-score standardization because it creates a normal distribution
Min-Max scaling is suitable when the algorithm is sensitive to the range of input values, as it scales all feature values into a specified range (usually 0-1). This ensures that all features have the same scale.
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