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.

Este ejercicio forma parte del curso

Anomaly Detection in Python

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Instrucciones del ejercicio

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.

Ejercicio interactivo práctico

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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))

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