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.
Questo esercizio fa parte del corso
Anomaly Detection in Python
Istruzioni dell'esercizio
- 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.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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))