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Comparison: Feature Extraction vs. Feature Selection

Feature extraction and feature selection are two key dimensionality reduction techniques in machine learning.

Aspect	Feature Extraction	Feature Selection
Definition	Transforms original features into new features.	Selects a subset of relevant features from the original dataset.
Approach	Creates new features by combining existing ones.	Removes irrelevant or redundant features without altering data.
Example Methods	PCA, LDA, Autoencoders.	Filter, Wrapper, Embedded methods.
Use Case	When features are correlated or redundant.	When some features are noisy or irrelevant.
Interpretability	Harder to interpret as new features are combinations.	Easier to interpret since original features remain intact.

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Subset Selection for Dimensionality Reduction

Feature selection reduces dimensionality by identifying and retaining the most informative features, eliminating irrelevant ones.

Subset Selection Techniques:

1. Filter Methods:

   • Uses statistical techniques (e.g., correlation, mutual information).

   • Example: Removing highly correlated features.

2. Wrapper Methods:

   • Uses model performance to evaluate feature subsets.

   • Example: Recursive Feature Elimination (RFE).

3. Embedded Methods:

   • Feature selection is done within model training.

   • Example: Lasso Regression (uses L1 regularization).

Example: Subset Selection in Text Classification

• Instead of using all words in a document, TF-IDF or Chi-Square selection picks only the most informative words, reducing dimensionality while maintaining accuracy.

Conclusion:
Feature selection improves model efficiency and interpretability by reducing computational complexity and overfitting, ensuring only the most relevant features are used.