We first examine the impact of the size of the training data set on the performance of the SVDD. We generated different-sized training data sets-100 instances of 10, 20, 50, 100, 150, 200, 300, 500 (m i , normal) pairs (still, σ H 2 O = 0.01 [RH] and σ m = 10 -5 [g gas/g ZIF]). For each training set, we train an SVDD, optimize its hyperparameters ν and γ, and evaluate it on a generated test data set of 100 sensor response vectors to normal conditions and 5 to each anomalous condition. Fig. shows the mean and standard error of the F1 score on the test set under different training set sizes. Increasing the size of the training data set improves the performance of the SVDD, with diminishing returns past a size of ∼200.