AutoML
Feature Selection
Introduced by Ghojogh et al. in Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewFeature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.
Source: Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Dimensionality Reduction | 31 | 5.75% |
| Classification | 31 | 5.75% |
| Feature Importance | 24 | 4.45% |
| Decision Making | 20 | 3.71% |
| Feature Engineering | 15 | 2.78% |
| Clustering | 12 | 2.23% |
| Image Classification | 11 | 2.04% |
| Fairness | 10 | 1.86% |
| Interpretable Machine Learning | 9 | 1.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
