About Feature Scaling and Normalization
Read OriginalThis article explains the concepts of feature scaling and normalization, crucial for many machine learning algorithms. It details standardization (Z-score) and Min-Max scaling, discusses when to use each method, and provides practical implementation examples using Python's scikit-learn library. It also explores the impact of scaling on algorithms like PCA and K-Nearest Neighbors.
Comments
No comments yet
Be the first to share your thoughts!
Browser Extension
Get instant access to AllDevBlogs from your browser
Top of the Week
1
Introducing GPT-5.1 for developers
Simon Willison
•
6 votes
2
A simple explanation of the big idea behind public key cryptography
Richard Gendal Brown
•
1 votes
3
Google Antigravity Exfiltrates Data
Simon Willison
•
1 votes
4
5
Fix “This video format is not supported” on YouTube TV
David Walsh
•
1 votes
6
Tooltip Components Should Not Exist
TkDodo Dominik Dorfmeister
•
1 votes
7
llm-anthropic 0.22
Simon Willison
•
1 votes
8
GPT-5.1 Instant and GPT-5.1 Thinking System Card Addendum
Simon Willison
•
1 votes
9
Nano Banana can be prompt engineered for extremely nuanced AI image generation
Simon Willison
•
1 votes
10
Hire Me in Japan
Dan Abramov
•
1 votes