Hyper-parameter Tuning of Data preparation steps

[amritvirsinghx]

Hi, In the book, it is mention that we can use some of the data preparation steps as hyper parameters eg add_bedrooms_per_room can be hyperparameter

I tried putting it directly in the GridSearchCV, but it gives an error.

from sklearn.model_selection import GridSearchCV
param_grid={
    'n_estimators':[3,10,30],'max_features':[2,4,6,8],
    'bootstrap':[False,True],'n_estimators':[3,10],'max_features':[2,3,4],
    'add_bedrooms_per_room':[True,False]
}

forest_reg=RandomForestRegressor()
grid_serach=GridSearchCV(forest_reg,param_grid,cv=5,scoring="neg_mean_squared_error")
grid_serach.fit(housing_prepared,housing_labels)

Can you please explain how to do it? and I want to tune this 'add_bedrooms_per_room':[True,False] and also use GridSearchCV to find out what can be the best strategy to fill missing values when we pass 'stategy':["mean","median"] etc

Please explain it using this example only: https://github.com/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb