Using modified z-scores with PyOD
It is time to unleash pyod on outliers. We use the MAD estimator from pyod to utilize modified z-scores. The estimator already uses the median_abs_deviation function under the hood, so it is unnecessary to repeat the previous steps.
The MAD estimator has already been loaded from pyod.models.mad and the data is available as prices.
Este exercício faz parte do curso
Anomaly Detection in Python
Instruções do exercício
- Initialize
MAD()with athresholdof 3.5. - Reshape
pricesto make it 2D. - Generate inlier/outlier labels on
pricesby fitting and predicting usingmadsimultaneously. - Subset
labelsfor outliers, which are denoted as 1.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Initialize with a threshold of 3.5
mad = ____(____=____)
# Reshape prices to make it 2D
prices_reshaped = ____.____(-1, 1)
# Fit and predict outlier labels on prices_reshaped
labels = ____
# Filter for outliers
outliers = ____[____ == ____]
print(len(outliers))