Elastic Net Regression Calculator

Enter your data below as comma-separated x,y pairs (one pair per line) or upload a CSV file. For multiple features, use format x1,x2,...,y with one data point per line. The calculator will find the best model and provide statistics about the model quality.

Data Input

Format: Each line should contain 1 feature values followed by 1 target value, all comma-separated.

Model Parameters

0.0110
Ridge (0)Lasso (1)

Elastic Net Regression

Elastic Net regression combines the L1 and L2 penalties of Lasso and Ridge regression to overcome their limitations. It's particularly useful when dealing with multiple correlated features.

How It Works

Elastic Net adds both L1 and L2 regularization terms to the linear regression objective function:

Minimize: RSS + ฮฑ ร— [(1-l1_ratio) ร— L2 + l1_ratio ร— L1]

Where:

  • RSS is the residual sum of squares
  • L1 is the sum of absolute values of coefficients
  • L2 is the sum of squared coefficients
  • ฮฑ controls the overall regularization strength
  • l1_ratio controls the balance between L1 and L2 regularization

When to Use Elastic Net Regression

Use Elastic Net when:

  • You have many correlated features
  • You want to perform feature selection (like Lasso) but also want to preserve groups of correlated variables
  • You want to avoid the limitations of both Ridge and Lasso regression

How to Use This Calculator

Enter your data below as comma-separated x,y pairs (one pair per line) or upload a CSV file. For multiple features, use format x1,x2,...,y with one data point per line. The calculator will find the best model and provide statistics about the model quality.