Many of the scikit-learn classifiers are equipped with a parameter
`class_weight` that can be helpful in situations such as this. Depending on
if you are on the development branch, or a public release, the preset
"auto" or "balanced" will re-weight samples by their inverse class
frequencies.
You may also do a grid search to try and find a "better" set of class
weights, something like this perhaps:
parameters = {'class_weight': [{A: i + 1., B: 10. - i} for i in
range(10)]}
clf = SomeClassifier()
grid = GridSearchCV(clf, parameters)
grid.fit(X, y)
- Trev
On Tue, Jun 23, 2015 at 7:25 AM, Neal Becker <ndbeck...@gmail.com> wrote:
> Any suggestions?
>
>
> Neal Becker wrote:
>
> > I am interested in supervised learning for classification where I have
> > multiple classes, but training data is highly unequal. There may be
> 1000s
> > of training examples for class A, but maybe 100s for class B. What are
> > suggested algorithms/approaches?
> >
> >
> >
>
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