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Balancing the Scale: Effective Scaling and Normalization Techniques in ML Data Preparation

Bragadeesh Sundararajan
9 min readJan 4, 2024

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In the field of machine learning, preprocessing data is a crucial step, and among the various preprocessing techniques, scaling and normalization stand out as particularly critical. These processes are essential for preparing raw data for input into machine learning models, especially those sensitive to feature scale.

Why Scaling and Normalization Matter

  1. Diverse Scales in Data: Real-world datasets often contain features with varying scales. For instance, one feature might represent age in years (ranging from 0 to 100), while another represents income in thousands of dollars. The disparity in these ranges can create issues during model training.
  2. Impact on Learning Process: Many machine learning algorithms are sensitive to the scale of the data. When features are on vastly different scales, algorithms tend to weight larger values more heavily, skewing the learning process. This can lead to models that are biased towards features with larger scales.
  3. Algorithms Particularly Affected:
  • Support Vector Machines (SVMs): SVMs optimize a margin and thus are heavily influenced by the scale of the input features.
  • K-means Clustering: Uses distance metrics…

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Bragadeesh Sundararajan
Bragadeesh Sundararajan

Written by Bragadeesh Sundararajan

Data strategist, coding aficionado, perpetual learner.

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