2012/5/11 Gael Varoquaux <[email protected]>: > On Fri, May 11, 2012 at 10:47:19PM +0900, Mathieu Blondel wrote: >> All algorithms which supports a warm_start constructor option should also >> be usable similarly to partial_fit. For example: > >> from sklearn.linear_model import Lasso > >> clf = Lasso(warm_start=True) >> clf.fit(X_subset1, y_subset1) >> clf.fit(X_subset2, y_subset2) >> ... > > I disagree: this is a very different thing than an online approach. In > the example above, 'clf' reflects on the data of the second subset if the > convergence has been successful.
+1 : the semantics of warm_start is *only to speedup the convergence* by starting from a solution closer to the optimal solution of the convex optimization problem (in this case the final solution will be the solution of fit(X_subsetn, y_subsetn) ignoring the data from the previous batches. partial_fit has a different semantics: the data seen in the first batches contribute to the final solution (generally using some kind of online averaging sometimes using an explicit learning rate schedule). -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
