Open amritvirsinghx opened 4 years ago
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
add_bedrooms_per_room
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
'add_bedrooms_per_room':[True,False]
'stategy':["mean","median"]
Please explain it using this example only: https://github.com/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb
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 hyperparameterI tried putting it directly in the GridSearchCV, but it gives an error.
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"]
etcPlease explain it using this example only: https://github.com/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb