Challenges in Feature Extraction
- Handling High-Dimensional Data
- Overfitting and Underfitting
- Computational Complexity
- Feature Redundancy and Irrelevance
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