GridSearchCV with Scikit Learn
The GridSearchCV
module from Scikit Learn provides many useful features to assist with efficiently undertaking a grid search. You will now put your learning into practice by creating a GridSearchCV
object with certain parameters.
The desired options are:
- A Random Forest Estimator, with the split criterion as 'entropy'
- 5-fold cross validation
- The hyperparameters
max_depth
(2, 4, 8, 15) andmax_features
('auto' vs 'sqrt') - Use
roc_auc
to score the models - Use 4 cores for processing in parallel
- Ensure you refit the best model and return training scores
You will have available X_train
, X_test
, y_train
& y_test
datasets.
This exercise is part of the course
Hyperparameter Tuning in Python
Exercise instructions
- Create a Random Forest estimator as specified in the context above.
- Create a parameter grid as specified in the context above.
- Create a
GridSearchCV
object as outlined in the context above, using the two elements created in the previous two instructions.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a Random Forest Classifier with specified criterion
rf_class = RandomForestClassifier(____=____)
# Create the parameter grid
param_grid = {____: ____, ____: ____}
# Create a GridSearchCV object
grid_rf_class = GridSearchCV(
estimator=____,
param_grid=____,
scoring=____,
n_jobs=____,
cv=____,
refit=____, return_train_score=____)
print(grid_rf_class)