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  3. Anomaly Detection in Python

Exercise

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.

Instructions

100 XP
  • 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.