Sklearn Fit model multiple times

Question

Origin of the problem is common:

presence of a lot of train data, which was read in chunks. Point of interest is to fit sequentially the desired model on chunked data sets, keeping states of previous fitting.

Are there any methods except partial_fit() to fit model using sklearn on different data? or is there any tricks to rewrite code of fit() function to customize it for this problem? or is it possible somekow realize with pickle?

Answer 1

There is a reason why some models expose partial_fit() and others don't. Every model is a different machine learning algorithm and for many of these algorithms there is just no way to add an element without recalculating the model from scratch.

So, if you have to fit the models incrementally, pick an incremental model that has partial_fit(). You can find a full list on this documentation page.

Alternatively, you can build an ensemble model. Create a separate Classifier() or Regression() for every chunk of data you have. Then, when you need to predict something, you can just

for classifier in classifiers:
  votes[classifier.predict(X)] += 1
prediction = numpy.argmax(votes)

or, for regressors

prediction = numpy.mean([regressor.predict(X) for regressor in regressors]