How is feature scaling important in algorithms like k-NN or SVM?

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ICSM Computer · Answer

Great question! Feature scaling is crucial for algorithms like
k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) because these methods rely heavily on the notion of distance or margins in feature space.
Why is feature scaling important?
1. Distance-based algorithms (like k-NN):
k-NN uses distance metrics (usually Euclidean distance) to find neighbors.If features have very different scales (e.g., one feature ranges 0-1, another 0-1000), features with larger ranges
dominate the distance calculation.This leads to biased neighbor selection, hurting performance.
2. Margin-based algorithms (like SVM):
SVM tries to find the hyperplane that maximizes the margin between classes.Features with larger scales can disproportionately influence the model and margin calculation.Without scaling, optimization may be inefficient or lead to suboptimal decision boundaries.
Common feature scaling techniques:
Technique
Description
Min-Max Scaling
Scales features to a fixed range, usually [0,1]
Standardization (Z-score)
Centers features to mean=0 and std=1
Summary:
Effect Without Scaling
Result
Different feature scales
Distance or margin calculations skewed
Features with large ranges dominate
Model bias and poor generalization
Poor convergence for SVM
Optimization difficulties
In short:
Scaling puts features on the same scale so distance and margin calculations are meaningful and balanced, leading to better and faster learning.

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How is feature scaling important in algorithms like k-NN or SVM?

Anubhav Kumar

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1 Answers

Why is feature scaling important?

1. Distance-based algorithms (like k-NN):

2. Margin-based algorithms (like SVM):

Common feature scaling techniques:

Summary:

In short:

Liked By

Technique	Description
Min-Max Scaling	Scales features to a fixed range, usually [0,1]
Standardization (Z-score)	Centers features to mean=0 and std=1

Effect Without Scaling	Result
Different feature scales	Distance or margin calculations skewed
Features with large ranges dominate	Model bias and poor generalization
Poor convergence for SVM	Optimization difficulties