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
.
This exercise is part of the course
Designing Machine Learning Workflows in Python
Exercise instructions
- Detect outliers in
features
using theeuclidean
metric. - Detect outliers in
features
using thehamming
metric. - Detect outliers in
features
using thejaccard
metric. - Find if all three metrics agree on any one outlier.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# 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(____ + ____ + ____ == ____))