In the context of feature scaling, what is the main purpose of normalization?

Practice Questions

Q1
In the context of feature scaling, what is the main purpose of normalization?
  1. To reduce the number of features
  2. To ensure all features contribute equally to the distance calculations
  3. To increase the variance of the dataset
  4. To eliminate outliers from the dataset

Questions & Step-by-Step Solutions

In the context of feature scaling, what is the main purpose of normalization?
  • Step 1: Understand that features are the different variables or attributes in your data.
  • Step 2: Realize that features can have different ranges (like age from 0-100 and income from 0-100,000).
  • Step 3: Know that when using algorithms like K-Means, the distance between data points matters.
  • Step 4: Recognize that if one feature has a much larger range, it can dominate the distance calculations.
  • Step 5: Learn that normalization adjusts the features to a similar range, usually between 0 and 1.
  • Step 6: Conclude that normalization ensures all features contribute equally to the calculations, improving the algorithm's performance.
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