Lasso Regression component

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.

Ridge Regression component Random Forest Regressor component

​Configuration

​Input / Output

​When to use

Configuration

Input / Output

When to use