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Practice

Feature Scaling and Preprocessing

In machine learning, feature scaling and data preprocessing ensure that all input features contribute equally to the model and are properly formatted for learning.

Without scaling, algorithms like KNN or gradient descent-based models can become biased toward features with larger numerical ranges.


Common Preprocessing Steps​

  • Feature Scaling: Normalize or standardize values so they're on a similar scale.
  • Encoding Categorical Variables: Convert text labels into numbers.
  • Handling Missing Values: Replace or remove nulls.
  • Feature Transformation: Apply mathematical transformations (log, polynomial, etc.).

Example: Standardization and Normalization​

Let’s see how to apply standardization and normalization using Scikit-learn’s preprocessing tools.

Scaling Features in Scikit-learn
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler

# Example dataset
X = np.array([[1.0, 200.0],
              [2.0, 300.0],
              [3.0, 400.0]])

# Standardization (mean=0, std=1)
scaler_std = StandardScaler()
X_std = scaler_std.fit_transform(X)

# Normalization (range [0, 1])
scaler_mm = MinMaxScaler()
X_mm = scaler_mm.fit_transform(X)

print("Standardized Data:", X_std)
print("Min-Max Scaled Data:", X_mm)

Choosing the Right Scaling Method​

The following are the two most common scaling methods:

  • Standardization — best for algorithms that assume normally distributed features (e.g., logistic regression, SVM).
  • Normalization — best for distance-based or gradient-sensitive models (e.g., KNN, neural networks).

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