  Disruption prediction and mitigation is of key importance in the development
of sustainable tokamakreactors. Machine learning has become a key tool in this
endeavour. In this paper multiple machinelearning models will be tested and
compared. A particular focus has been placed on their portability.This
describes how easily the models can be used with data from new devices. The
methods used inthis paper are support vector machine, 2-tiered support vector
machine, random forest, gradient boostedtrees and long-short term memory. The
results show that the support vector machine performanceis marginally better
among the standard models, while the gradient boosted trees performed the
worst.The portable variant of each model had lower performance. Random forest
obtained the highest portableperformance. Results also suggest that disruptions
can be detected as early as 600ms before the event.An analysis of the
computational cost showed all models run in less than 1ms, allowing sufficient
timefor disruption mitigation.
