Update GridSearch.py

GridSearch.py

@@ -1,32 +1,28 @@
-# Tell which hyperparameters you want it to experiment with and what values to try out
-# It will evaluate all the possible combinations of hyperparameter values, using cross-validation.
+# Input which hyperparameters what values to try out.
+# It will run all combinations of hyperparameter values with cross-validation.
 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]},
+{'n_estimators': [#, #, #], 'max_features': [#, #, #, #]},
+{'bootstrap': [False], 'n_estimators': [#, #], 'max_features': [#, #, #]},
 ]
 forest_reg = RandomForestRegressor()
-grid_search = GridSearchCV(forest_reg, param_grid, cv=5,
+grid_search = GridSearchCV(forest_reg, param_grid, cv=#,
 scoring='neg_mean_squared_error',
 return_train_score=True)
-grid_search.fit(housing_prepared, housing_labels)
-# All in all, the grid search will explore 12 + 6 = 18 combinations of RandomForestRegressor hyperparameter values, and it will train each model five times (since we are
-# using five-fold cross validation). In other words, all in all, there will be 18 × 5 = 90 rounds of training!
+grid_search.fit(data_prepared, data_labels)
+# The grid search above will explore different combinations of RandomForestRegressor hyperparameter values, and it will train each model # times (depending on
+# how many folds cross validation you are using). 
 
-# Finally, you can get the best combination of parameters like this:
+# Finally, get the best parameters:
 grid_search.best_params_
 
 grid_search.best_estimator_
 
 # Bonus:
-#If GridSearchCV is initialized with refit=True (which is the default), then once it finds the best estimator using crossvalidation,
-# it retrains it on the whole training set. This is usually a good idea since feeding it more data will likely improve its performance.
+# Initialize the GridSearchCV with refit=True (which is the default), it retrain the model on the whole training set with the best hyperparameter values found.
 
 # One step further: Randomized Search
-# The grid search approach is fine when you are exploring relatively few combinations, like in the previous example, but when the hyperparameter search space is large, it is
-# often preferable to use RandomizedSearchCV instead. This class can be used in much the same way as the GridSearchCV class, but instead of trying out all possible combinations,
-# it evaluates a given number of random combinations by selecting a random value for each hyperparameter at every iteration.
+# when the hyperparameter search space is large, it is often preferable to use RandomizedSearchCV.
 # This approach has two main benefits:
-# • If you let the randomized search run for, say, 1,000 iterations, this approach will explore 1,000 different values for each hyperparameter (instead of just a few values
-# per hyperparameter with the grid search approach).
-# • You have more control over the computing budget you want to allocate to hyperparameter search, simply by setting the number of iterations.
+# • Able to explore more values per hyperparameter. Much larger search space.
+# • Setting the number of iterations to have more control over computing time.