Tuning other hyperparameters
The power of GridSearchCV really comes into play when you're tuning multiple hyperparameters, as then the algorithm tries out all possible combinations of hyperparameters to identify the best combination. Here, you'll tune the following random forest hyperparameters:
| Hyperparameter | Purpose |
|---|---|
| criterion | Quality of Split |
| max_features | Number of features for best split |
| max_depth | Max depth of tree |
| bootstrap | Whether Bootstrap samples are used |
The hyperparameter grid has been specified for you, along with a random forest classifier called clf.
This exercise is part of the course
Marketing Analytics: Predicting Customer Churn in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import GridSearchCV
from sklearn.model_selection import GridSearchCV
# Create the hyperparameter grid
param_grid = {"max_depth": [3, None],
"max_features": [1, 3, 10],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
# Call GridSearchCV
grid_search = ____(___,___,cv=3)