Generalized Linear Models
Logistic Regression
Logistic Regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.
Source: scikit-learn
Image: Michaelg2015
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 63 | 13.15% |
| General Classification | 61 | 12.73% |
| Feature Selection | 18 | 3.76% |
| Federated Learning | 13 | 2.71% |
| Semantic Segmentation | 12 | 2.51% |
| Decision Making | 12 | 2.51% |
| Time Series | 11 | 2.30% |
| Sentiment Analysis | 11 | 2.30% |
| Real-Time Object Detection | 10 | 2.09% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |