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

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Exercise instructions

Fit (only fit) the IForest() estimator to big_mart.
Access the training labels and store them as labels.
Use pandas subsetting on big_mart to filter the outliers into outliers.

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))

Edit and Run Code