OOB Score vs Test Set Score
Now that you instantiated bc
, you will fit it to the training set and evaluate its test set and OOB accuracies.
The dataset is processed for you and split into 80% train and 20% test. The feature matrices X_train
and X_test
, as well as the arrays of labels y_train
and y_test
are available in your workspace. In addition, we have also loaded the classifier bc
instantiated in the previous exercise and the function accuracy_score()
from sklearn.metrics
.
This exercise is part of the course
Machine Learning with Tree-Based Models in Python
Exercise instructions
Fit
bc
to the training set and predict the test set labels and assign the results toy_pred
.Evaluate the test set accuracy
acc_test
by callingaccuracy_score
.Evaluate
bc
's OOB accuracyacc_oob
by extracting the attributeoob_score_
frombc
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit bc to the training set
____.____(____, ____)
# Predict test set labels
y_pred = ____.____(____)
# Evaluate test set accuracy
acc_test = ____(____, ____)
# Evaluate OOB accuracy
acc_oob = ____.____
# Print acc_test and acc_oob
print('Test set accuracy: {:.3f}, OOB accuracy: {:.3f}'.format(acc_test, acc_oob))