[scikit-learn] Error while using GridSearchCV.

[Shubham Singh Tomar]

Hi, guys!

Thanks for the responses.

@Fernando: Yes, this code is, in fact, part of Udacity's Boston Housing
project. I'm currently working on my MLE Nanodegree.

I was able to modify the code to go with *sklearn.model_selection*, as you
suggested.
And, it's great to see you help Udacity students here as well :)

Do you think we should update the code and project description in main
Udacity repository to support the newer sklearn versions?

On Wed, Mar 8, 2017 at 2:32 AM, Fernando Marcos Wittmann <
fernando.wittmann at gmail.com> wrote:

> Hey Shubham,
>
> I am a project reviewer at Udacity. This code seems to be part of one of
> our projects (P1 - Boston Housing
> <https://github.com/WittmannF/Machine_Learning-Boston_Housing/blob/master/boston_housing.ipynb>).
> I think that you have updated the old module sklearn.cross_validation to
> the module sklearn.model_detection, is that correct? If yes, then you
> should also update the parameters in ShuffleSplit to match with this new
> version (check the docs
> <http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html>).
> Try to update ShuffleSplit to the following line of code:
>
>     cv_sets = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
>
> I hope that helps! Feel free to send me a PM.
>
>
> On Tue, Mar 7, 2017 at 10:24 AM, Shubham Singh Tomar <
> tomarshubham24 at gmail.com> wrote:
>
>> Hi,
>>
>> I'm trying to use GridSearchCV to tune the parameters for
>> DecisionTreeRegressor. I'm using sklearn 0.18.1
>>
>> I'm getting the following error:
>>
>> ---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-36-192f7c286a58> in <module>()      1 # Fit the training data to the model using grid search----> 2 reg = fit_model(X_train, y_train)      3       4 # Produce the value for 'max_depth'      5 print "Parameter 'max_depth' is {} for the optimal model.".format(reg.get_params()['max_depth'])
>> <ipython-input-35-500141c331d9> in fit_model(X, y)     11      12     # Create cross-validation sets from the training data---> 13     cv_sets = ShuffleSplit(X.shape[0], n_splits = 10, test_size = 0.20, random_state = 0)     14      15     # TODO: Create a decision tree regressor object
>> TypeError: __init__() got multiple values for keyword argument 'n_splits'
>>
>>
>>
>>
>> --
>> *Thanks,*
>> *Shubham Singh Tomar*
>> *Autodidact24.github.io <http://Autodidact24.github.io>*
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>>
>
>
> --
>
> Fernando Marcos Wittmann
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
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>
>


-- 
*Thanks,*
*Shubham Singh Tomar*
*Autodidact24.github.io <http://Autodidact24.github.io>*
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