| finding | value |
|---|---|
| Model Quality | Good fit (R² > 0.7) |
| Alpha (Mixing) | 0.5 — Elastic Net (alpha=0.5) |
| Variables Selected | 4 of 6 predictors |
| Variables Excluded | 2 predictors set to 0 |
| R-Squared | 83.4% |
| RMSE | 5.035 |
| Optimal Lambda | 1.0006 |
This executive summary evaluates the Elastic Net regression model's ability to identify predictive variables and forecast outcomes. The analysis demonstrates how regularization techniques balance model complexity with predictive accuracy—critical for understanding whether the selected variables justify business investment and whether the model is ready for deployment.
The model achieves strong explanatory power while successfully performing automatic feature selection. The four retained predictors—led by predictor_1 (
Analysis overview and configuration
| Parameter | Value | _row |
|---|---|---|
| alpha | 0.5 | alpha |
| n_folds | 10 | n_folds |
| lambda_choice | lambda.1se | lambda_choice |
| standardize | TRUE | standardize |
This Elastic Net regression analysis identifies which of six advertising channels drive sales for Test Company. The model uses regularization (alpha=0.5) to balance feature selection with prediction accuracy, selecting the optimal complexity via 10-fold cross-validation. Understanding the model setup and performance validates whether the analysis reliably answers the business question.
The model demonstrates reliable identification of advertising channel
Data preprocessing and column mapping
| Metric | Value |
|---|---|
| Initial Rows | 300 |
| Final Rows | 300 |
| Rows Removed | 0 |
| Retention Rate | 100% |
This section documents the data preprocessing pipeline for the Elastic Net regression analysis. It shows that all 300 observations were retained without any rows removed during cleaning, indicating either pristine input data or minimal preprocessing requirements. Understanding data retention is critical for assessing whether the final model is trained on a representative sample and whether any systematic data loss could bias the regression results.
The perfect retention rate suggests the input dataset was clean and complete, with no missing values, duplicates, or quality issues requiring remediation. This is favorable for model reliability, as the full sample size of 300 observations supports the Elastic Net's ability to estimate 6 predictors and achieve an R² of 0.834. However, the absence of documented train/test splits means model performance metrics (RMSE=5.035, MAE=4.106) reflect cross-validated estimates rather than holdout validation.
The 10-fold cross-
Non-zero coefficients at the selected lambda — the variables chosen by Elastic Net
This section identifies which variables drive the outcome and quantifies their individual impact. By showing non-zero coefficients at the optimal regularization level (lambda=1.001), it reveals the final set of predictors the model retained after balancing fit quality with simplicity. Understanding variable importance is essential for interpreting model behavior and identifying the key drivers of predictions.
The model achieves R²=0.834 using only 4
Actual vs predicted scatter plot showing model fit quality
This section evaluates how accurately the Elastic Net model captures the relationship between predictors and the outcome variable. The metrics quantify prediction accuracy and explain how much variance in the target is accounted for by the selected features, which is essential for assessing whether the model is suitable for practical application.
The model demonstrates solid predictive performance with predictions clustering near the 45-degree diagonal. The high R² combined with moderate error metrics suggests the Elastic Net successfully identified the 4 most influential predictors while regularization prevented overfitting. The balanced residual