L1 regularization for automatic feature selection using glmnet with 10-fold cross-validation to find optimal lambda.
Sets coefficients to exactly zero, reporting selected vs zeroed features with sparsity ratio.
10-fold CV with lambda.min selection, plus lambda.1se for conservative option.
Visualize coefficient evolution across lambda values with CV error curves.
Provide a dataset with a numeric target and array of features. Features are automatically scaled using scale() before fitting.
Uses glmnet with alpha=1 (pure Lasso), reports R², RMSE, MAE, selected features count, sparsity ratio, and generates comprehensive diagnostic plots including Q-Q plots and residual analysis.
From preprocessing to validated model
Standardize predictors using scale() to ensure equal penalty application.
10-fold CV with cv.glmnet to find optimal lambda.min and lambda.1se values.
Get coefficients, calculate metrics (R², RMSE, MAE), generate diagnostic plots.
Achieve automatic feature selection with exact zero coefficients—reducing model complexity while maintaining predictive power.
The implementation uses glmnet with pure Lasso (alpha=1), provides both lambda.min and lambda.1se options, reports sparsity ratio showing percentage of features eliminated, and includes comprehensive diagnostics with coefficient paths, CV error curves, Q-Q plots, and residual analysis.
Note: Uses lambda.min by default. Features are always scaled. For grouped feature selection with correlated predictors, consider Elastic Net instead.
Get sparse, validated models with clear insights
Read the article: Ridge vs Lasso