Hi
I have written a method pairwise_distances_argmin_min_n in my "develop"
mode.
Functionality is similar to pairwise_distances_argmin_min, but, it returns
n minimas
rather than only one (both indices and the minimas). And it does it in
chunk mode (parallel) on sparse matrices which needed some st
Hello All,
I am new to scikitlearn and ML, and trying to train my model using random
forest and gradient boosting trees regressors. I was wondering what is the
best way to do hyperparameter tuning, shall I use GridSearchCV or
RandomisedSearchCV? I have read that the performance of RandomiseSeacrhCV
Hi, Waseem,
with a fine-enough grid, the GridSearchCV would be more "thorough" than the
randomized search. However, the problem is essentially some sort of
combinatorial explosion. Typically, I start with a "rougher" grid (the
different parameters are more "spaced out" relative to each other). A
Hi Sebastian,
Thanks for your reply. So this mean I should start with e.g. "max_depth":
[1,4,10,15], "min_samples_leaf":[1,10,20,30]. and if the max_depth=10 and
min_samples_leaf=10, then I should explore values close to these values. Am
I right?
Shall I use small value of number of estimator, whi
> Thanks for your reply. So this mean I should start with e.g. "max_depth":
> [1,4,10,15], "min_samples_leaf":[1,10,20,30]. and if the max_depth=10 and
> min_samples_leaf=10, then I should explore values close to these values. Am I
> right?
Yes, this would work. However, keep in mind that you
> Thanks for your reply. So this mean I should start with e.g. "max_depth":
> [1,4,10,15], "min_samples_leaf":[1,10,20,30]. and if the max_depth=10 and
> min_samples_leaf=10, then I should explore values close to these values. Am
> I right?
>
>
> Yes, this would work. However, keep in mind that you
I meant, how I make sure that I don't miss the "Good" combination that you
mentioned?
Also, for second point: Maybe considering computational time and then
making sure that I have enough number of estimators in the parametric
study?
On Fri, Jan 29, 2016 at 9:38 PM, muhammad waseem
wrote:
>
> Th
> On Jan 29, 2016, at 4:45 PM, muhammad waseem wrote:
>
> I meant, how I make sure that I don't miss the "Good" combination that you
> mentioned?
Here, we are back to an exhaustive search on an infinitely small grid :). It's
really about finding the "sweet spot" that is "practical" given you
On Fri, Jan 29, 2016 at 9:51 PM, Sebastian Raschka
wrote:
>
> On Jan 29, 2016, at 4:45 PM, muhammad waseem
> wrote:
>
> I meant, how I make sure that I don't miss the "Good" combination that you
> mentioned?
>
>
> Here, we are back to an exhaustive search on an infinitely small grid :).
> It's r
