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Analyze another fileElastic net finds which variables actually predict your outcome by combining two regularization techniques. It automatically drops unimportant predictors and handles situations where your variables are correlated with each other — giving you a simpler, more reliable model than plain regression.
Use this when you have many potential predictors and want to find out which ones actually matter, especially if some predictors are correlated.
If you only have 2-3 predictors and no multicollinearity, use standard linear regression. If you want to keep ALL predictors (no selection), use Ridge regression instead.
Built for: Data scientist, marketing analyst, pricing analyst, researcher
Typical data source: A CSV with one numeric outcome column (sales, revenue, score) and multiple numeric predictor columns (spend channels, features, measurements)
Marketing spend data with channel-level ad spend and resulting sales
Minimum 30 rows · Best with 200-5000 rows
Elastic net regression combines L1 (lasso) and L2 (ridge) penalties to handle multicollinearity and perform automatic feature selection. Fits a regularized model via glmnet, selects optimal alpha and lambda via cross-validation, and reports coefficients, variable importance, and prediction accuracy.
Shows how prediction error changes as regularization increases. The dashed line marks the chosen lambda — left of it is overfitting, right is underfitting.
The predictors that survived selection and their effect sizes. Larger bars = stronger influence on the outcome. Zero-coefficient predictors were dropped as unimportant.
Predicted vs actual values. Points near the diagonal line = good predictions. Clusters far off = the model misses something in those cases.
Ranks predictors by how much they influence the outcome. Use this to prioritize — the top variables are where to focus your attention or budget.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Linear Regression — Few predictors, no multicollinearity, want straightforward coefficients
Need more power? Random Forest — Need non-linear relationships, feature interactions, or classification
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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