Support Vector Machine

What it does

Fits maximum-margin models with linear, RBF or polynomial kernels.

Use it for medium-sized datasets with complex boundaries.

Data you need

• Target
• Numeric features

Supported data: Numeric. Level: Advanced.

Formula and method

min ½‖w‖² + C Σ ξᵢ

After standardization on the training rows, the solver finds the maximum-margin boundary, or ε-insensitive regression function, by quadratic programming. Kernels allow a non-linear boundary in the original space.

How to read the result

Larger C fits the training data more closely.

Worked example

Illustrative run on Fisher iris measurements. These figures are the actual output of this model on that example, and they say nothing about your own data.

• Test accuracy = 0.9474 against a majority-class baseline of 0.3421; F1 = 0.9487.
• The model is better than always predicting the most common class on this test set.

Limitations

• Slow on large datasets

Numerical validation

Validated 15 of 15 automated checks passed, including numerical results, invalid input, missing values, small samples, constant variables, charts and the interpretation text.

Computed with python 3.13.2, numpy 2.2.5, scipy 1.14.1, pandas 2.3.3, statsmodels 0.14.4, scikit-learn 1.7.0, in your browser. A passing check confirms the listed comparison only. It does not make any result suitable for your data, and it is not a claim of equivalence with other statistical software.

Questions

Do I need to scale features?

Yes, and it is done automatically, fitted on the training data only.