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.wittm...@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 < > tomarshubha...@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@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > > -- > > Fernando Marcos Wittmann > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- *Thanks,* *Shubham Singh Tomar* *Autodidact24.github.io <http://Autodidact24.github.io>*
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