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Because SCML optimization procedure is based on stochastic subgradient descent, we can save the weights after fitting the model, and use them in a following fit call (with a different set of triplets). The decision to use the
warm_start
parameter instead of a newpartial_fit
method is because partial_fit in scikit-learn will only fit 1 epoch, whereas fit will fit for multiple epochs (until the loss converges or max_iter is reached), which is the case also for SCML.