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:
Transforms data into principal components (new uncorrelated features)
Helps visualize high-dimensional data
Reduces overfitting and improves model performance
Applications:
Image compression
Data visualization
Noise reduction in datasets
Advantages:
Reduces computational cost
Removes redundant features
Helps in better visualization
Limitations:
Principal components are hard to interpret
May lose some important information
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