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What is the main goal of dimensionality reduction techniques like PCA?
Practice Questions
Q1
What is the main goal of dimensionality reduction techniques like PCA?
To increase the number of features
To improve model accuracy
To reduce the number of features while preserving variance
To create new features from existing ones
Questions & Step-by-Step Solutions
What is the main goal of dimensionality reduction techniques like PCA?
Steps
Concepts
Step 1: Understand that data often has many features (or dimensions).
Step 2: Realize that having too many features can make analysis difficult and slow.
Step 3: Learn that dimensionality reduction techniques help simplify the data.
Step 4: Know that PCA (Principal Component Analysis) is one such technique.
Step 5: Understand that PCA reduces the number of features while keeping the important information.
Step 6: Remember that the goal is to keep as much variance (or information) in the data as possible.
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