Isolation Forest

What is Isolation Forest?

Isolation Forest is an unsupervised machine learning algorithm used for anomaly detection. It is based on the idea that anomalies are few and different, so they are easier to isolate from the rest of the data. Isolation Forest works by recursively partitioning the feature space using random splits, eventually isolating each data point. Anomalies are generally isolated faster than normal data points, leading to shorter paths in the tree structure.

How does Isolation Forest work?

Isolation Forest works by building multiple binary trees, called isolation trees. Each tree is constructed by repeatedly selecting a random feature and a random split value, partitioning the data accordingly. The process is repeated until each data point is isolated or a certain depth limit is reached. The anomaly score for each data point is calculated based on the average path length in the isolation trees. Points with shorter average path lengths are considered more likely to be anomalies.

Example of Isolation Forest in Python:

To use Isolation Forest in Python, you can use the scikit-learn library:

import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.datasets import make_blobs

# Generate some sample data with a few anomalies
n_samples = 100
n_anomalies = 5
X, _ = make_blobs(n_samples=n_samples - n_anomalies, random_state=42)
X = np.concatenate([X, np.random.uniform(low=-10, high=10, size=(n_anomalies, 2))], axis=0)

# Fit the Isolation Forest model
isolation_forest = IsolationForest(contamination=float(n_anomalies) / n_samples)

# Get anomaly scores and predict anomalies
anomaly_scores = isolation_forest.decision_function(X)
anomaly_predictions = isolation_forest.predict(X)

Additional resources on Isolation Forest: