The User Guide has an example that better illustrates what Andy meant: for
continuous parameters such as C and gamma in a gaussian kernel SVM, you should
use a continuous distribution (e.g. exponential):
http://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization
Vlad
> On 20 Apr 2015, at 15:34, Vlad Niculae <zephy...@gmail.com> wrote:
>
> 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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