Support Vector 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

0.110
0.011
0.011

Support Vector Regression (SVR)

Support Vector Regression applies the principles of Support Vector Machines (SVMs) to regression problems. It aims to find a function that deviates from the observed values by a value no greater than a specified margin, while being as flat as possible.

How It Works

SVR works by mapping the input data into a higher-dimensional feature space where it can be described by a linear model. The algorithm tries to find a function that:

  • Has at most ฮต deviation from the actual target values
  • Is as flat as possible (minimizes complexity)
  • Allows for some errors using a penalty parameter C

The key parameters in SVR are:

  • C: Controls the trade-off between model complexity and allowing errors
  • ฮต (epsilon): Defines the width of the insensitive tube around the function
  • Kernel: Determines how the data is transformed (linear, RBF, polynomial)
  • Gamma: Defines the influence of each training example (for RBF kernel)

When to Use SVR

Use Support Vector Regression when:

  • You're dealing with non-linear relationships
  • Your dataset has a moderate size (SVR can be computationally intensive for very large datasets)
  • You want a model that is robust to outliers
  • You need good generalization performance

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.