A simple outlier

When you first encounter a new type of algorithm, it is always a great idea to test it with a very simple example. So you decide to create a list containing thirty examples with the value 1.0 and just one example with value 10.0, which you expect should be flagged as an outlier. To make sure you use the algorithm correctly, you convert the list to a pandas dataframe, and feed it into the local outlier factor algorithm. pandas is available to you as pd.

This exercise is part of the course

Designing Machine Learning Workflows in Python

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Exercise instructions

Import the LocalOutlierFactor module as lof for convenience.
Create a list with thirty 1s followed by a 10, [1.0, 1.0, ..., 1.0, 10.0].
Cast the list to a data frame.
Print the outlier scores produced by the local outlier factor algorithm.

Hands-on interactive exercise

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

# Import the LocalOutlierFactor module
from sklearn.____ import ____ as lof

# Create the list [1.0, 1.0, ..., 1.0, 10.0] as explained
x = ____*30
x.____(10)

# Cast to a data frame
X = pd.____(x)

# Fit the local outlier factor and print the outlier scores
print(lof().____(X))

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Designing Machine Learning Workflows in Python

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In the previous chapters you established a solid foundation in supervised learning, complete with knowledge of deploying models in production but always assumed you a labeled dataset would be available for your analysis. In this chapter, you take on the challenge of modeling data without any, or with very few, labels. This takes you into a journey into anomaly detection, a kind of unsupervised modeling, as well as distance-based learning, where beliefs about what constitutes similarity between two examples can be used in place of labels to help you achieve levels of accuracy comparable to a supervised workflow. Upon completing this chapter, you will clearly stand out from the crowd of data scientists in confidently knowing what tools to use to modify your workflow in order to overcome common real-world challenges.

Exercise 1: Anomaly detection Exercise 2: A simple outlier

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Exercise 3: LoF contamination Exercise 4: Novelty detection Exercise 5: A simple novelty Exercise 6: Three novelty detectors Exercise 7: Contamination revisited Exercise 8: Distance-based learning Exercise 9: Find the neighbor Exercise 10: Not all metrics agree Exercise 11: Unstructured data Exercise 12: Restricted Levenshtein Exercise 13: Bringing it all together Exercise 14: Concluding remarks