Dimensionality Reduction
A technique to reduce the number of features in a dataset while preserving its essential information.
What is Dimensionality Reduction?
– Improves computational efficiency
– Reduces noise and redundancy
– Improves model performance
– Facilitates visualization
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Why is it Important?
– Principal Component Analysis (PCA)
– Linear Discriminant Analysis (LDA)
– t-SNE
– UMAP
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Popular Techniques
– Find the principal components (directions of maximum variance).
– Project the data onto these components.
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How PCA Works
– Image and video processing
– Natural language processing
– Bioinformatics
– Customer segmentation
Applications of Dimensionality Reduction
Consider factors like:
– Type of data (numerical, categorical)
– Goal of analysis (visualization, classification)
– Computational resources
Choosing the Right Technique
Unlock the power of data simplification and gain valuable insights.
Master Dimensionality Reduction