Hi everybody and merry Christmas.
I wanted to ask what people think about the future of the generalized
cross-validation API.
Currently, estimators that make some generalized cross-validation
possible provide a EstimatorCV class,
(RidgeCV, RFECV, LassoLarsCV).
I think we should decide whether
Hi Andreas!
... and Merry Christmas to all!
Quick and naive question: what is the point in cross-validating the
number of trees in RandomForest (or in Extra-Trees)? The rule simple
is simple: the more, the better.
Gilles
On 25 December 2012 13:07, Andreas Mueller amuel...@ais.uni-bonn.de
On 12/25/2012 01:24 PM, Gilles Louppe wrote:
Hi Andreas!
... and Merry Christmas to all!
Quick and naive question: what is the point in cross-validating the
number of trees in RandomForest (or in Extra-Trees)? The rule simple
is simple: the more, the better.
Ok, maybe RandomForest was a bad
Second, what do you exactly mean by generalized CV? I am not sure to have
the same idea in mind. Do you mean finding the best parameter value without
brute force, in a smart way specific to the estimator?
In that case, one could do that on min_samples_split, using a post pruning
procedure.
On 12/25/2012 01:40 PM, Gilles Louppe wrote:
Second, what do you exactly mean by generalized CV? I am not sure to
have the same idea in mind. Do you mean finding the best parameter
value without brute force, in a smart way specific to the estimator?
Basically yes. Something that fits an
hi,
the CV models in coordinate_descent have the same use case. We use
warm restarts to fit efficiently for many values of alpha. The way it is done
is via a path function that returns a list of models fitted sequentially.
Then there is cv loop that runs the path for every fold and picks the
best
