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Analyze another fileLASSO regression shrinks less important predictor coefficients to exactly zero, performing automatic variable selection. Gives you a sparse model with only the predictors that matter.
Use this when you have many predictors and want a model that selects the important ones automatically.
If correlated predictors should stay together, use Ridge or Elastic Net instead.
Built for: Data scientist, researcher, analyst
Typical data source: Numeric outcome with multiple numeric predictors
Regression data with outcome and predictors
Minimum 30 rows · Best with 200-5000 rows
LASSO (Least Absolute Shrinkage and Selection Operator) regression with automatic variable selection via L1 regularization. Identifies the most important predictors by shrinking less relevant coefficients to exactly zero. Includes regularization path, cross-validation lambda selection, coefficient importance chart, and model fit diagnostics.
Coefficient shrinkage as regularization increases
MSE vs lambda with optimal lambda.min and lambda.1se markers
Non-zero coefficients at optimal lambda (variables selected by LASSO)
Model fit with predicted values plotted against actual outcomes
R-squared, RMSE, MAE, and number of selected variables
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Linear Regression — Few predictors, no regularization needed
Need more power? Elastic Net — Correlated predictors — need L1+L2 combined
Similar: Ridge
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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