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)
...
Another idea is to learn a different classifier on each subset and use a
mixture of the classifiers. As a mixture weight, a simple choice is 1 /
n_mixtures.
clf = LinearSVC()
clf.fit(X_subset1, y_subset1)
clf2 = LinearSVC()
clf2.fit(X_subset2, y_subset2)
clf.coef_ += clf.coef_
...
clf.coef_ /= n_mixtures
HTH,
Mathieu
------------------------------------------------------------------------------
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
