[scikit-learn] Tuning custom parameters using grid_search

[Jacob Schreiber]

You can use a pipeline object to contain both feature
selection/transformation steps and an estimator. All elements of a pipeline
can then be tuned using gridsearch. You can see a simple example here:
http://scikit-learn.org/stable/modules/pipeline.html

You may also be interested seeing if the FeatureUnion object can serve the
same purpose as your FeatureMultiplier.

On Wed, Sep 7, 2016 at 2:03 PM, Piotr Bialecki <piotr.bialecki at hotmail.de>
wrote:

> Hi all,
>
> I am currently tuning some parameters of my xgboost model using scikit's
> grid_search, e.g.:
>
> param_test1 = {'max_depth':range(3,10,2),
>                            'min_child_weight':range(1,6,2)
> }
> gsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1,
> n_estimators=762,
>
>             max_depth=5, min_child_weight=1, gamma=0,
>
>              subsample=0.8, colsample_bytree=0.8,
>
>              objective= 'binary:logistic', nthread=4,
>
>              scale_pos_weight=1, seed=2809),
>                                             param_grid = param_test1,
>                                             scoring='roc_auc',
>                                             n_jobs=6,
>                                             iid=False, cv=5)
>
> Before that I preprocessed my dataset X with some different methods.
> These preprocessing steps have some parameters too, which I would like to
> tune.
> I know that it is possible to tune the parameters of the preprocessing
> steps,
> if they are part pf my pipeline.
> E.g. if I am using PCA, I could tune the parameter n_components, right?
>
> But what if I have some "custom" preprocessing code with some parameters?
> Is it possible to create a scikit-compatible "object" of my custom code
> in order to tune the
> parameters in the pipeline with grid search?
> Imagine I would like to write a custom method FeatureMultiplier() with a
> parameter multiplier_value.
> Is it possible to create a scikit-compatible class out of this method and
> tune it with grid search?
>
> I thought I saw a talk about exactly this topic at some PyData in 2016 or
> 2015,
> but unfortunately I cannot find the video of it.
> Maybe I misunderstood the presentation at that time.
>
>
> Best regards,
> Piotr
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20160907/35ee4e64/attachment.html>