Storing outlier probabilities
Continue building the ensemble by writing the code block that loops over estimators and generates outlier probabilities.
The scaled apple dataset with extra features is available.
This exercise is part of the course
Anomaly Detection in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
estimators = [IForest(n_estimators=50), IForest(n_estimators=100)]
shape = (len(apple), len(estimators))
probability_scores = np.empty(shape=shape)
for ____, ____ in ____:
# Fit the estimator
____