Hi Nadim, you may also want to take a look at *skope-rules* ( https://github.com/scikit-learn-contrib/skope-rules), which has recently been added to scikit-learn-contrib.
The main goal of this package is to provide logical rules verifying precision and recall conditions, by extracting them from a fitted tree ensemble and evaluating them out of bag. Nicolas On Fri, Mar 16, 2018 at 5:25 AM, Andreas Mueller <t3k...@gmail.com> wrote: > Hi. > > Unfortunately we don't have an implementation of a cost matrix in sklearn > directly, but you can change the threshold of the model prediction, > by using something like y_pred = tree.predict_proba(X_test)[:, 1] > 0.6 > > What trade-off of precision and recall do you want? Have you looked at the > precision_recall_curve? > > Andy > > > On 03/15/2018 09:28 PM, Nadim Farhat wrote: > > Dear All, > > I have a *screening* lab test and I am trying to minimize the False > negative value in the recall (TP/(TP+FN)) therefore I want to increase the > cost whenever an FN is found in the training. I understand that in R they > have some kind of loss matrix that penalize the FN during fitting. my > Postive classes percentage is 30 % > On the forums and StackOverflow, they suggest using class_weight=balanced > in the decision tree which oversamples the class with the lowest > frequency. However, I don't see how that helps in minimizing the FN. > > Any suggestions? > > > Bests > > Nadim > > > > > > > > > > -- > Nadim Farhat > > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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