Detecting outliers with IForest
IForest
is a robust estimator and only requires a few lines of code to detect outliers from any dataset. You may find that this syntax looks familiar since it closely resembles sklearn
syntax.
The full version of the Big Mart Sales data has been loaded for you as big_mart
, which you can explore in the console.
This exercise is part of the course
Anomaly Detection in Python
Exercise instructions
- Import the
IForest
estimator frompyod
. - Initialize an
IForest()
with default parameters. - Fit the estimator and generate predictions on the
big_mart
simultaneously, and store the results inlabels
. - Use
pandas
subsetting to filter out the outliers frombig_mart
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import IForest from pyod
from pyod.____ import ____
# Initialize an instance with default parameters
iforest = ____
# Generate outlier labels
labels = ____
# Filter big_mart for outliers
outliers = ____
print(outliers.shape)