KNN for the first time

You will practice KNN for the first time on a version of the Ansur Body Measurements dataset for females. The female version also contains 95 columns but only 1.9k observations.

The dataset has been loaded as females into the environment.

Este exercício faz parte do curso

Anomaly Detection in Python

Ver curso

Instruções do exercício

Import the KNN estimator from the relevant pyod module.
Instantiate a KNN() estimator with 0.5% contamination and 20 neighbors as knn.
Create a boolean index named is_outlier that returns True when the labels_ of knn return 1.
Isolate the outliers from females using is_outlier into outliers.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Import KNN from the relevant pyod module
from pyod.____ import ____

# Instantiate KNN and fit to females
knn = KNN(____, ____, n_jobs=-1)
knn.____

# Create a boolean index that checks for outliers
is_outlier = ____

# Isolate the outliers
outliers = ____

print(len(outliers))

Editar e executar o código