Dimensionality reduction is a process used in machine learning to reduce the number of features or variables in a dataset while preserving important information. This simplifies the dataset, making it easier to visualize and analyze.
- Glossary > Letter: D
What does "Dimensionality Reduction" mean?

Use Cases
Data Visualization:
Reducing high-dimensional data to two or three dimensions for easier visualization.
Noise Reduction::
Removing irrelevant features that may introduce noise and affect model performance.
Speed Improvement:
Decreasing computational load by working with fewer features.

Importance
Simplifies Models:
Makes models easier to interpret and faster to train.
Reduces Overfitting:
Minimizes the risk of overfitting by eliminating redundant and irrelevant features.
Enhances Performance:
Can improve model accuracy by focusing on the most significant features

Analogies
Dimensionality reduction is like packing for a trip. Instead of taking everything from your closet, you choose a few essential items that meet your needs, making your luggage lighter and more manageable.
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