I have been using both in time-series classification. I put a exponential decay in sample_weights AND class weights as a dictionary.
BR/Schots Em sex., 4 de dez. de 2020 às 12:01, Nicolas Hug <nio...@gmail.com> escreveu: > Basically passing class weights should be equivalent to passing > per-class-constant sample weights. > > > why do some estimators allow to pass weights both as a dict in the init > or as sample weights in fit? what's the logic? > > SW is a per-sample property (aligned with X and y) so we avoid passing > those to init because the data isn't known when initializing the estimator. > It's only known when calling fit. In general we avoid passing data-related > info into init so that the same instance can be fitted on any data (with > different number of samples, different classes, etc.). > > We allow to pass class_weight in init because the 'balanced' option is > data-agnostic. Arguably, allowing a dict with actual class values violates > the above argument (of not having data-related stuff in init), so I guess > that's where the logic ends ;) > > As to why one would use both, I'm not so sure honestly. > > > Nicolas > > > On 12/4/20 10:40 AM, Sole Galli via scikit-learn wrote: > > Actually, I found the answer. Both seem to be optimising the loss function > for the various algorithms, below I include some links. > > If, we pass *class_weight* and *sample_weight,* then the final cost / > weight is a combination of both. > > I have a follow up question: in which scenario would we use both? why do > some estimators allow to pass weights both as a dict in the init or as > sample weights in fit? what's the logic? I found it a bit confusing at the > beginning. > > Thank you! > > > https://stackoverflow.com/questions/30805192/scikit-learn-random-forest-class-weight-and-sample-weight-parameters > > > https://stackoverflow.com/questions/30972029/how-does-the-class-weight-parameter-in-scikit-learn-work/30982811#30982811 > > Soledad Galli > https://www.trainindata.com/ > > > ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ > On Thursday, December 3, 2020 11:55 AM, Sole Galli via scikit-learn > <scikit-learn@python.org> <scikit-learn@python.org> wrote: > > Hello team, > > What is the difference in the implementation of class_weight and > sample_weight in those algorithms that support both? like random forest or > logistic regression? > > Are both modifying the loss function? in a similar way? > > Thank you! > > Sole > > > > > _______________________________________________ > 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 > -- Schots
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