Benefits of Isolation Forest for outliers detection | Data Science Interview Questions

Benefits of Isolation Forest for outliers detection | Data Science Interview Questions | Machine Learning

Isolation Forest is a very efficient, especially in high-dimensional data. It requires fewer trees to achieve better performance than other algorithms like Local Outlier Factor (LOF) and One-Class SVM (Support Vector Machine). It does not require any distance or density calculations, which can be computationally expensive for high-dimensional data

It is not affected by the presence of irrelevant features.

It an detect both global and local outliers, making it a versatile algorithm for outlier detection.

Isolation Forest is insensitive to the scale of the data, which means it can handle datasets with different scales without requiring any normalization or scaling.

It is robust to noise and does not assume any distribution for the data.

It can handle mixed types of data (numeric and categorical)

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