Logistic Regression models the probability of each class using a linear combination of features. It’s fast, interpretable, and works well when the relationship between features and target is approximately linear.Documentation Index
Fetch the complete documentation index at: https://docs.clickml.app/llms.txt
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Configuration
| Parameter | Description | Default |
|---|---|---|
| C | Inverse of regularization strength. Smaller values = stronger regularization. | 1.0 |
| Solver | Optimization algorithm: lbfgs, liblinear, saga, sag, newton-cg | lbfgs |
| Max Iterations | Maximum number of iterations for the solver to converge. | 1000 |
| Penalty | Regularization type: l2, l1, elasticnet, none | l2 |
| Random State | Seed for reproducibility. | 42 |
Input / Output
| Type | |
|---|---|
| Input | X Train + Y Train |
| Output | Trained Model |