> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clickml.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Logistic Regression Classifier component

> Train a linear logistic regression model for binary and multi-class classification, with L1 and L2 regularization and configurable solvers.

**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.