Hi Andy,
I'm following up on this from a while ago, because I finally got around to
trying to implement it.
To get the minority f1 score from my grid search, I have:
min_f_scorer = make_scorer(f1_score(pos_label=1))
grid_search = GridSearchCV(pipeline, parameters, verbose=1, cv=3,
I have recently been using grid search to evaluate a custom method for
dimensionality reduction (DR) along with supervised and unsupervised
estimators later in the pipeline to discover its usefulness.
gr = grid_search.GridSearchCV(
pipeline
, param_grid, cv = None)
The scoring functions I used
Sorry, I meant https://github.com/scikit-learn/scikit-learn/issues/4301
On 18 May 2015 at 12:10, Joel Nothman joel.noth...@gmail.com wrote:
Sorry, grid search (and similar) does not support clusterers. This
probably should be formally tracked as an issue.
Sorry, grid search (and similar) does not support clusterers. This probably
should be formally tracked as an issue.
https://github.com/scikit-learn/scikit-learn/issues/4040 might be helpful
to you.
On 18 May 2015 at 11:56, Jitesh Khandelwal jk231...@gmail.com wrote:
I have recently been using
Hi Sam,
I think this could be interesting. You could allow for learning parameters
on each sub-cluster by accepting a transformer as a parameter, then using
sample = sklearn.base.clone(transformer).fit_transform(sample).
I suspect bisecting k-means is notable enough and different enough for
