Support Vector Machine Quiz

Explore margins, kernels, support vectors, classification, regression, tuning, and diagnostics through practical challenges with instant feedback and visual progress reports for confident learning success.

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Detailed review

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Core formulas

Boundary: wᵀx + b = 0
Signed distance: (wᵀx+b)/||w||
Full canonical margin: 2/||w||
Hinge loss: max(0, 1-yf(x))
RBF kernel: exp(-γ||x-z||²)
Polynomial kernel: (γxᵀz+r)^d
OVO classifiers: k(k-1)/2
SVR loss: max(0, |y-f(x)|-ε)

Tuning guidance

Large C penalizes violations strongly. Large gamma creates local influence. Tune both with cross-validation inside a preprocessing pipeline.

Common workflow

Split data safely.
Build preprocessing inside a pipeline.
Establish a linear baseline.
Tune C, gamma, kernel, and class weights.
Evaluate once on the final test set.

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Support Vector Machine Quiz

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