Book Review - Hands on Machine Learning Chapter 2 Code review3

Code Review handles concepts, unfamiliar codes for myself

All of materials(quotes, images, definitions) are from this book.
It’s all just for self-study.

Full codes are in my github repository

1. Fine Tune Model

In this step, all I need to do is tell in which hyperparameters I want it to experiment with, and what values to try out, and it will evaluate all the possible combinations of hyperparameter values using cross_validation

1. GridSerachCV

from sklearn.model_selection import GridSearchCV
param_grid = [
              {'n_estimators': [3,10,30], 'max_features':[2,4,6,8]},
              {'bootstrap': [False], 'n_estimators': [3,10], 'max_features': [2,3,4]},
]
forest_reg = RandomForestRegressor()
grid_search = GridSearchCV(forest_reg, param_grid, cv = 5,
                           scoring = 'neg_mean_squared_error',
                           return_train_score = True)
grid_search.fit(housing_prepared, housing_labels)

Need more study about GridSearchCV

2. Evaluate the test set

final_model = grid_search.best_estimator_
X_test = strat_test_set.drop('median_house_value', axis = 1)
y_test = strat_test_set['median_house_value'].copy()
# transform using full_pipeline
X_test_prepared = full_pipeline.transform(X_test)
final_predictions = final_model.predict(X_test_prepared)
final_mse = mean_squared_error(y_test, final_predictions)
final_rmse = np.sqrt(final_mse)

3. Hard parts for me

• custom transformation : class CombinedAttributesAdder
• Pipeline
• GridSearchCV

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