> 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 may be missing a
> "good" combination this way. And if you have a large number of
> n_estimators, tuning a random forest can be "relatively" expensive. Plus,
> you'd typically don't want or need to prune the trees here, that's
> basically the whole idea behind RF.
>
So I make sure that I don't miss the "Good" combination?
>
> 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?
>
>
> Also here, the parameters that you tuned may only be good for the model
> based on the specific number of estimators. In general, I would maybe
> advice against tuning the hyperparameters at all and use the computational
> time to increase the number of n_estimators.
>
Maybe considering computational time and then making sure that I have
enough number of estimators in the parametric study?
>
> On Jan 29, 2016, at 4:18 PM, muhammad waseem <m.waseem.ah...@gmail.com>
> wrote:
>
> 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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--
Dr Muhammad Waseem Ahmad
Research Associate,
BRE Center for Sustainable Construction,
School of Engineering,
Cardiff University,
Cardiff, UK.
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