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.
Questo esercizio fa parte del corso
Anomaly Detection in Python
Istruzioni dell'esercizio
- Instantiate
KNNwith 20 neighbors. - Calculate outlier probabilities.
- Create a boolean mask that returns true values where the outlier probability is over 55%.
- Use
is_outlierto filter the outliers fromfemales.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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))