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.

Это упражнение является частью курса

Anomaly Detection in Python

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Инструкции к упражнению

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.

Интерактивное практическое упражнение

Попробуйте выполнить это упражнение, дополнив этот пример кода.

# 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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