Z-score normalization is a statistical data preprocessing technique that transforms raw data into a standard scale, expressing values in terms of their deviation from the mean, measured in standard deviations. This process standardizes feature distributions, which is crucial for machine learning models.
Mechanism
Each data point undergoes transformation by subtracting the mean of its respective feature and subsequently dividing by that feature’s standard deviation. The outcome is transformed data characterized by a mean of zero and a standard deviation of one, enabling fair comparison across features with disparate scales.
Methodology
This technique is extensively applied in quantitative finance and crypto analytics to prepare diverse datasets for algorithmic trading, risk modeling, or anomaly detection. Z-score normalization facilitates faster convergence and more consistent performance of machine learning algorithms, especially when features possess varying scales or units.
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