Hi Isaac, You may have a look at MiniBatchKMeans and MiniBatchDictionaryLearning that both proposes this API. At the moment, you should fit a single mini batch to the estimator using partial_fit, and update the inner attributes accordingly. During the first partial_fit, you should take care of various memory allocation that are needed by the estimator.
Please fill free to create a pull request whenever you think your code is ready for review. Good luck! Le 26 mai 2016 13:14, <donkey-ho...@cryptolab.net> a écrit : > hello scikit-learn devs, > > After following the work on IsolationForest so far and testing on a > real-world problem here we've found this model to be very promising for > anomaly detection. However, at present, IsolationForest only fits data in > batch even while it may be well suited to incremental on-line learning > since one could subsample recent history and older estimators can be > dropped progressively. > > I'd like to contribute this feature, but being new to ML and scikit-learn > I'm curious how I should start making a quick & dirty version to see how > this may work. Are there other good examples where one could see the > difference between .fit and .partial_fit in other models? > > thanks > isaak y. > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
