Data Science, Machine Learning, Support Vector Machine, Python.
this, we need to add a “slack variable” to allow some training examples to be misclassified or to fall within the margin, which can make the SVM decision boundary smoother and more robust to noise. So, our contraints becomes like this yᵢ(wᵀ)xᵢ + b ≥ 1 — ξ And the optimization problem becomes: