What is the significance of max_iter in sklearn.cluster.MiniBatchKMeans? Is this the maximum number of times partial_fit() can be executed on batches of data?
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$\begingroup$ I think when we train MiniBatchKMeans model using partial_fit() in incremental manner, max_iter doesn't really have any significance. $\endgroup$ – NewDev Jul 8 '20 at 3:06
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max_iterint, default=100
Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.
It's the number of iteration over the full dataset.
Number of partial_fit will depend on batch_size
batch_sizeint, default=100
Size of the mini batches.
partial_fit(self, X[, y, sample_weight])
Update k means estimate on a single mini-batch X.
Ref :
User guide
Class Def.
