Understanding Isolation Forest
Isolation Forest is an efficient and scalable algorithm for detecting outliers in high-dimensional datasets. Rather than profiling normal data points and identifying deviations, it works by isolating anomalies. Outliers are easier to isolate because they tend to differ significantly from most of the data. The algorithm randomly selects a feature and splits the data based on a random threshold; fewer splits are typically needed to isolate anomalies.
This method is particularly well-suited for large datasets and is capable of both outlier and novelty detection, making it a versatile tool in the ML toolkit. This recipe utilizes Isolation Forest to identify both inlier and outliers in datasets.
Getting ready
We’ll generate a synthetic dataset that includes visible outliers. This will allow us to compare the performance of Isolation Forest against the known distribution.
Load the libraries:
import numpy as np import matplotlib.pyplot as plt from sklearn...
