KNN with outlier probabilities

Since we cannot wholly trust the output when using contamination, let's double-check our work using outlier probabilities. They are more trustworthy.

The dataset has been loaded as females and KNN estimator is also imported.

This exercise is part of the course

Anomaly Detection in Python

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Exercise instructions

Instantiate KNN with 20 neighbors.
Calculate outlier probabilities.
Create a boolean mask that returns true values where the outlier probability is over 55%.
Use is_outlier to filter the outliers from females.

Hands-on interactive exercise

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

# Instantiate a KNN with 20 neighbors and fit to `females`
knn = ____
knn.____

# Calculate probabilities
probs = ____

# Create a boolean mask
is_outlier = ____

# Use the boolean mask to filter the outliers
outliers = ____

print(len(outliers))

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