AI Terms Dictionary

Definition

Common methods include Min-Max scaling and Z-score standardization. This process ensures that features with larger magnitudes do not dominate the learning algorithm, particularly in gradient-based optimization like neural networks. By normalizing input data, models train faster and achieve better stability. It is a critical step in preparing datasets for machine learning pipelines to ensure equitable contribution from all variables.

Summary

Normalization is a data preprocessing technique that scales numerical features to a standard range, typically between 0 and 1, to improve model convergence and performance.

Key Concepts

• Min-Max Scaling
• Z-Score Standardization
• Feature Scaling
• Gradient Descent Stability

Use Cases

• Preprocessing image pixel values
• Preparing tabular data for neural networks
• Improving regression model accuracy

Code Example

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from sklearn.preprocessing import MinMaxScaler
import numpy as np
data = np.array([[10], [20], [30]])
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(data)

Related Terms

• Standardization
• Data Preprocessing
• Feature Engineering