Feature Selection vs. Feature Extraction
Aspect | Feature Selection | Feature Extraction |
---|---|---|
Definition | Selecting a subset of relevant features from the original set | Transforming the original features into a new set of features |
Purpose | Reduce dimensionality | Transform data into a more manageable or informative representation |
Process | Filtering, wrapper methods, embedded methods | Signal processing, statistical techniques, transformation algorithms |
Input | Original feature set | Original feature set |
Output | Subset of selected features | New set of transformed features |
Information Loss | May discard less relevant features | May lose interpretability of original features |
Computational Cost | Generally lower than feature extraction | May be higher, especially for complex transformations |
Interpretability | Retains interpretability of original features | May lose interpretability depending on transformation |
Examples | Forward selection, backward elimination, LASSO | Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Autoencoders |
What is Feature Extraction?
The process of machine learning and data analysis requires the step of feature extraction. In order to select features that are more suited for modeling, raw data must be chosen and transformed.
In this article we will learn about what is feature extraction, why is it important.
Table of Content
- Understanding Feature Extraction
- Why is Feature Extraction Important?
- Different types of Techniques for Feature Extraction
- 1. Statistical Methods
- 2. Dimensionality Reduction Methods for feature extraction
- 3. Feature Extraction Methods for Textual Data
- 4. Signal Processing Methods
- 5. Image Data Extraction
- Feature Selection vs. Feature Extraction
- Applications of Feature Extraction
- Tools and Libraries for Feature Extraction
- Benefits of Feature Extraction
- Challenges in Feature Extraction