Support Vector Machine Calculator

Calculate SVM parameters, support vectors, margin width, and complexity for support vector machines.

C Parameter

Gamma (RBF)

Kernel Type

Polynomial Degree

Number of Features

Number of Samples

Tab to move between fields

Estimated Support Vectors

100

Margin Width

Training Complexity1,000,000

Prediction Complexity1,000

Effective Dimensionality10

Regularization Strength1

How to Use This Calculator

Enter the number of training samples and feature dimensions.
Select the kernel type (linear, RBF, polynomial) based on data separability.
Input the regularization parameter C and kernel-specific parameters (gamma for RBF, degree for polynomial).
Review the estimated support vector count and decision boundary margin.
Use cross-validation to tune C and gamma — a grid search over log-scale values is standard practice.

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Formula

Margin = 2 / ||w||

Calculate accuracy, precision, recall, F1 score, MCC, AUC, and other classification performance metrics.

Calculate optimization convergence, computational cost, efficiency, and memory requirements for ML optimizers.

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