In [1]:

    

# Using a helper function to do Cross Validation automatically.
# Similar to how other sklearn objects are wrapped in helpers like
# pipelines.


    

In [2]:

    

from sklearn import ensemble
rf = ensemble.RandomForestRegressor(max_features='auto')


    

In [3]:

    

from sklearn import datasets
X, y = datasets.make_regression(10000, 10)


    

In [4]:

    

from sklearn import cross_validation


    

In [5]:

    

scores = cross_validation.cross_val_score(rf, X, y)


    

In [6]:

    

print scores


    

    




[ 0.84909057  0.84418761  0.85055925]

In [7]:

    

scores = cross_validation.cross_val_score(rf, X, y, verbose=3,
                                          cv=4)


    

    




[CV] no parameters to be set .........................................
[CV] ................ no parameters to be set, score=0.845672 -   0.6s
[CV] no parameters to be set .........................................
[CV] ................ no parameters to be set, score=0.852102 -   0.6s
[CV] no parameters to be set .........................................
[CV] ................ no parameters to be set, score=0.845743 -   0.6s
[CV] no parameters to be set .........................................
[CV] ................ no parameters to be set, score=0.846676 -   0.6s





    




[Parallel(n_jobs=1)]: Done   1 jobs       | elapsed:    0.6s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    2.4s finished






    

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