Using outlier probabilities
An alternative to isolating outliers with contamination is using outlier probabilities. The best thing about this method is that you can choose an arbitrary probability threshold, which means you can be as confident as you want in the predictions.
IForest and big_mart are already loaded.
Este ejercicio forma parte del curso
Anomaly Detection in Python
Instrucciones del ejercicio
- Calculate probabilities for both inliers and outliers.
- Extract the probabilities for outliers into
outlier_probs. - Filter the outliers into
outliersby using a 70% threshold onoutlier_probs.
Ejercicio interactivo práctico
Prueba este ejercicio completando el código de muestra.
iforest = IForest(random_state=10).fit(big_mart)
# Calculate probabilities
probs = iforest.____
# Extract the probabilities for outliers
outlier_probs = ____[____]
# Filter for when the probability is higher than 70%
outliers = ____[____]
print(len(outliers))