LOF for the first time

LOF differs from KNN only in the internal algorithm and the lack of the method parameter. Practice detecting outliers with it using contamination filtering on the scaled version of females dataset from previous exercises.

The dataset has been loaded as females_transformed.

This exercise is part of the course

Anomaly Detection in Python

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Exercise instructions

Import the LOF estimator from the relevant pyod module.
Instantiate an LOF() with 0.3% contamination, 20 neighbors and n_jobs set to -1.
Create a boolean index that returns True values when the labels_ returned from lof are equal to 1.
Isolate the outliers from females_transformed using is_outlier.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import LOF from its relevant module
from pyod.____ import ____

# Instantiate LOF and fit to females_transformed
lof = ____
lof.____

# Create a boolean index that checks for outliers
is_outlier = ____

# Isolate the outliers
outliers = ____

print(len(outliers))

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