Isolation Forest Model


Isolation Forest is an unsupervised ensemble learning algorithm for anomaly detection that works on the principle of isolating anomalies in the leaves


To use the algorithm you need to specify the number of estimators (the base number is 100), as well as the contamination (can have values between 0.1 and 0.5, default is 0.1)
notion image


  • In most cases, it is highly recommended to stick with a default contamination value (0.1)