We wish to find (ν opt , γ opt ), the ν and γ pair that minimizes Λ(ν, γ) over the training data. Each time we evaluate Λ(ν, γ), we must train an SVDD with a given (ν, γ) and employ the MC procedure to estimate the area inside the decision boundary. Thus, using a simple minimization method such as grid search is inefficient. Instead, we use Bayesian optimization (BO) to find a near-optimal (ν, γ) using the fewest evaluations of Λ (ν, γ). BO is a sequential search that builds a Gaussian process surrogate model of the objective function at each iteration, then automatically selects the next (ν, γ) at which to evaluate Λ(ν, γ) based on the greatest expected improvement in the (negative) objective function. The mean of the surrogate model is our prediction for Λ(ν, γ), and its variance is a measure of the uncertainty. BO uses the surrogate model and the expected improvement acquisition function to balance exploration and exploitation in its decision-making. Importantly, each time BO queries Λ, the surrogate model is updated.