Normalization

Adjusting data values to a common scale

Overview

Normalization is the process of adjusting numbers in a dataset to use a common scale, without distorting the differences in ranges. It's like converting different measurements (inches, centimeters, meters) to all use the same unit.

Why Normalize Data?

Normalization helps:

  • Make different features comparable
  • Improve model training
  • Speed up learning
  • Reduce bias from large numbers
  • Make patterns more visible

Common Methods

  • Min-Max Scaling
    • Converts to 0-1 range
    • Preserves zero values
    • Handles different ranges
  • Decimal Scaling
    • Moves decimal point
    • Keeps numbers simple
    • Maintains relationships

When to Use It

  • Working with neural networks
  • Comparing different measurements
  • Processing images
  • Handling sensor data
  • Combining different scales

Best Practices

  • Choose the right method
  • Handle outliers first
  • Keep scaling consistent
  • Document your process
  • Validate results