Principal Component Analysis (PCA)
📘 Artificial Intelligence & Machine Learning Basics
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📅 Nov 05, 2025
⏱ Estimated reading time: 1 min
Definition:
A dimensionality reduction technique used in machine learning to reduce the number of features while retaining most of the data’s variation.
Key Points:
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Transforms data into principal components (new uncorrelated features)
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Helps visualize high-dimensional data
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Reduces overfitting and improves model performance
Applications:
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Image compression
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Data visualization
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Noise reduction in datasets
Advantages:
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Reduces computational cost
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Removes redundant features
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Helps in better visualization
Limitations:
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Principal components are hard to interpret
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May lose some important information
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