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Not all metrics agree

In the previous exercise you saw that not all metrics agree when it comes to identifying nearest neighbors. But does this mean they might disagree on outliers, too? You decide to put this to the test. You use the same data as before, but this time feed it into a local outlier factor outlier detector. The module LocalOutlierFactor has been made available to you as lof, and the data is available as features.

Diese Übung ist Teil des Kurses

Designing Machine Learning Workflows in Python

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Anleitung zur Übung

  • Detect outliers in features using the euclidean metric.
  • Detect outliers in features using the hamming metric.
  • Detect outliers in features using the jaccard metric.
  • Find if all three metrics agree on any one outlier.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Compute outliers according to the euclidean metric
out_eucl = ____(metric='euclidean').fit_predict(features)

# Compute outliers according to the hamming metric
out_hamm = ____(metric=____).fit_predict(features)

# Compute outliers according to the jaccard metric
out_jacc  = ____(____=____).____(features)

# Find if the metrics agree on any one datapoint
print(any(____ + ____ + ____ == ____))
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