The example you cite contains these lines:
"max_features": sp_randint(1, 11),
"min_samples_split": sp_randint(1, 11),
"min_samples_leaf": sp_randint(1, 11),
Those are not lists, but distribution objects from scipy (see at the top of the
example, `from scipy.stats import randint as sp_randint`).
RandomizedSearchCV takes this kind of input, but GridSearchCV does not. As Andy
says, this kind of parametrization is what you should use if you intend to do
randomized parameter search. You can use other distributions from scipy.stats
as well, if more appropriate.
Vlad
> On 20 Apr 2015, at 15:16, Pagliari, Roberto <rpagli...@appcomsci.com> wrote:
>
> Yes, I agree. From the example, though, my understanding is that you can only
> pass arrays, not functions, isn't that true?
>
> Thank you,
>
> ________________________________________
> From: Andreas Mueller [t3k...@gmail.com]
> Sent: Monday, April 20, 2015 2:55 PM
> To: scikit-learn-general@lists.sourceforge.net
> Subject: Re: [Scikit-learn-general] randomized grid search
>
> If you have continuous parameter you should really really really use
> continuous distributions!
>
> On 04/20/2015 12:58 PM, Pagliari, Roberto wrote:
>> Hi Vlad,
>> when using randomized grid search, does sklearn look into intermediate
>> values, or does it samples from the values provided in the parameter grid?
>>
>> Thank you,
>>
>> ________________________________________
>> From: Vlad Niculae [zephy...@gmail.com]
>> Sent: Monday, April 20, 2015 12:50 PM
>> To: scikit-learn-general@lists.sourceforge.net
>> Subject: Re: [Scikit-learn-general] randomized grid search
>>
>> Hi Roberto
>>
>>> what does None do for max_depth?
>> Copy-pasted from
>> http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
>>
>> "If None, then nodes are expanded until all leaves are pure or until all
>> leaves contain less than min_samples_split samples.”
>>
>>> In particular, if lists are provided, does randomized grid search construct
>>> a uniform probability distribution?
>> Yes
>>
>>> If that's the case, I presume there is no advantage over GridSearchCV?
>> You still get roughly the same advantages (if some parameters matter way
>> more than others, you can get the good scores faster), as long as the grid
>> you’re randomly sampling from is large enough. But if you have more informed
>> distributions to specify, that’s even better.
>>
>> For convenience, when I have computing power and time to spare, I often run
>> a few tens/hundreds iterations of RandomSearch on large discrete grids, and
>> if it seems promising, I run a full GridSearch overnight with minimal
>> changes to the code.
>>
>> For practical purposes, it would probably be a better use of the time to
>> just do more random search, but if this would go into a paper, for some
>> audiences it can be more convincing to say you searched a grid thoroughly.
>>
>> Hope this makes sense,
>> Vlad
>>
>>> Thank you,
>>>
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