cross_val_score has created three different models for cross-validation.
Which did you want to use to impute?
After cross-validation you can fit the model on the whole dataset, although
this may be bad practice depending on how you want to use the model.
GridSearchCV is the common way to use cross
import numpy as np
from sklearn.datasets import load_boston from sklearn.ensemble import
RandomForestRegressor from sklearn.pipeline import Pipeline from
sklearn.preprocessing import Imputer from sklearn.cross_validation import
cross_val_score
rng = np.random.RandomState(0)
dataset = load_boston
