> ## 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.

# Lasso Regression component

> Train a linear regression model with L1 regularization that shrinks unimportant coefficients to zero, performing automatic feature selection.

**Lasso** (Least Absolute Shrinkage and Selection Operator) applies L1 regularization, which drives some coefficients exactly to zero. This means Lasso simultaneously trains a model and selects features.

## Configuration

| Parameter          | Description                                                         | Default |
| ------------------ | ------------------------------------------------------------------- | ------- |
| **Alpha**          | Regularization strength. Higher = more coefficients pushed to zero. | `1.0`   |
| **Fit Intercept**  | Whether to include a bias/intercept term.                           | `true`  |
| **Max Iterations** | Maximum solver iterations.                                          | `1000`  |
| **Random State**   | Seed for reproducibility.                                           | `42`    |

## Input / Output

|        | Type                  |
| ------ | --------------------- |
| Input  | `X Train` + `Y Train` |
| Output | Trained Model         |

## When to use

Use when you suspect only a subset of your features are truly predictive and want the model to identify them automatically. **Scale features** before using Lasso.