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.
Este ejercicio forma parte del curso
Anomaly Detection in Python
Instrucciones del ejercicio
- 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.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# 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))