Standardization
Transforming data to have zero mean and unit variance
Overview
A data preprocessing technique that transforms numeric values to have a mean of zero and a standard deviation of one. Unlike normalization which scales values to a fixed range, standardization adjusts data to follow a normal distribution pattern. This is particularly useful for machine learning algorithms that work best with normally distributed data.
Core Concepts
- Centering data around zero
- Scaling by standard deviation
- Preserving outliers
- Maintaining data relationships
- Supporting statistical analysis
Implementation Methods
- Standard Z-Score Method
- Subtracts the mean
- Divides by standard deviation
- Creates zero-centered data
- Robust Method
- Uses median instead of mean
- Better handles extreme values
- More stable with outliers
Common Applications
- Medical Data Analysis
- Lab result comparisons
- Vital signs monitoring
- Patient data analysis
- Model Development
- Improves machine learning accuracy
- Supports predictive analytics
- Enhances model performance
Best Practices
- Data Preparation
- Check data quality
- Handle missing values
- Document parameters
- Quality Control
- Validate results
- Monitor transformations
- Ensure consistency