Alternative way of classifying with IForest
Until now, you have been using the .fit_predict()
method to fit IForest
and generate predictions simultaneously. However, pyod
documentation suggests using the fit
function first and accessing the inlier/outlier labels_
via a handy attribute.
You will practice this on the big_mart
dataset.
This exercise is part of the course
Anomaly Detection in Python
Exercise instructions
- Fit (only
fit
) theIForest()
estimator tobig_mart
. - Access the training labels and store them as
labels
. - Use
pandas
subsetting onbig_mart
to filter the outliers intooutliers
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
iforest = IForest(n_estimators=200)
# Fit (only fit) it to the Big Mart sales
____
# Access the labels_ for the data
labels = iforest.____
# Filter outliers from big_mart
outliers = ____[____]
print(len(outliers))