Exploring Random Forest Hyperparameters
Understanding what hyperparameters are available and the impact of different hyperparameters is a core skill for any data scientist. As models become more complex, there are many different settings you can set, but only some will have a large impact on your model.
You will now assess an existing random forest model (it has some bad choices for hyperparameters!) and then make better choices for a new random forest model and assess its performance.
You will have available:
X_train,X_test,y_train,y_testDataFrames- An existing pre-trained random forest estimator,
rf_clf_old - The predictions of the existing random forest estimator on the test set,
rf_old_predictions
This exercise is part of the course
Hyperparameter Tuning in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print out the old estimator, notice which hyperparameter is badly set
print(____)
# Get confusion matrix & accuracy for the old rf_model
print("Confusion Matrix: \n\n {} \n Accuracy Score: \n\n {}".format(
confusion_matrix(____, ____),
accuracy_score(____, ____)))