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, while conducting this
parametric study.After that I can use a higher value while fitting my
model? Will this change other parameters, meaning is n_estimator depends on
other parameters?
Also, should I use early stopping while doing GridSearchCV?
Thanks again.
Regards
Waseem
On Fri, Jan 29, 2016 at 6:57 PM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> 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). After
> that, I use a "finer" grid around the parameters that came up in the
> previous search.
> However, it all comes down to computational time vs. being thorough. Or in
> other words, grid search is an exhaustive search whereas randomized search
> is a computationally "more efficient" approach.
>
>
> > On Jan 29, 2016, at 11:45 AM, muhammad waseem <m.waseem.ah...@gmail.com>
> wrote:
> >
> > 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
> is almost same as GridSearchCV (most of the times). If I go with
> RandomisedSearchCV then what should be the range of values for different
> parameters? How will I know that the range I am selecting is the correct
> one?
> >
> > Also, what about the number of estimators? In the GridSearchCV or
> RandomisedSearchCV, shall I start with a low value and then after selecting
> other parameters, I will choose a large number of estimators for fitting
> purposes. Am I right?
> >
> > Shall I always use early stopping, no matter if I use Grid search or
> Randomised Search?
> >
> > P.S: Training data: Number of Inputs = 6
> > Number fo Outputs = 1
> > Number of samples (rows) = 8526
> > testing data: Number of samples (rows) = 1416
> >
> > Thanks
> > Kindest Regards
> > Waseem
> >
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