Logistic Regression Classifier component - ClickML Studio

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.

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

When to use

Best for linearly separable data. Apply Scaling and Encoding before this component, as it is sensitive to feature magnitudes and requires numerical inputs.