Feature Scaling (Normalization & Standardization)

📏 Feature Scaling in Machine Learning

Feature scaling ensures that numerical features are on a similar scale, which helps models train faster and more accurately.

🚨 Some models (like KNN, SVM, neural networks) can be seriously affected by unscaled data!

🔁 Why Scale Features?

• Prevent features with large values from dominating the learning process.
• Improve convergence in gradient-based methods (like neural networks).
• Ensure distance-based algorithms (e.g., K-Means, KNN) behave properly.

⚖️ Normalization vs Standardization

🔹 1. Normalization (Min-Max Scaling)

📌 Formula:

x′=x−xminxmax−xminx' = \frac{x - x_{\text{min}}}{x_{\text{max}} - x_{\text{min}}}

• Rescales values to a [0, 1] range
• Sensitive to outliers
• Also called Min-Max Scaling

✅ Best For:

• When you know the bounds of your features
• Algorithms like KNN, Neural Networks

🔸 2. Standardization (Z-score Scaling)

📌 Formula:

x′=x−μσx' = \frac{x - \mu}{\sigma}

• Rescales data to have mean = 0 and standard deviation = 1
• Not bounded — values can be negative or greater than 1
• More robust to outliers (compared to normalization)

✅ Best For:

• When data is normally distributed
• Algorithms like SVM, Logistic Regression, Linear Regression

📊 Quick Comparison Table:

💡 Bonus Tip:

Use sklearn.preprocessing:

from sklearn.preprocessing import MinMaxScaler, StandardScaler

# Normalization
minmax = MinMaxScaler()
X_scaled = minmax.fit_transform(X)

# Standardization
standard = StandardScaler()
X_scaled = standard.fit_transform(X)

🧠 TL;DR:

• Normalize if you need bounded values (0 to 1)
• Standardize if your data looks Gaussian or has outliers
• Always scale your training and test data using the same parameters!

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