Semi-Supervised Outlier Detection Using PyOD

Outlier detection is a crucial step in many data science problems in any field. A lot of outlier detection methods have developed and applied in real life, ranging from univariate descriptive statistics to the use of machine learning/deep learning for multivariate outlier detection.

In this article, we will focus on utilizing various machine learning methods to perform outlier detection in multivariate data. PyOD is the main library used in this article because of its ease in applying various methods according to their respective characteristics.

This article will also introduce Semi Supervised method for anomaly detection, including its application on porosity, permeability and density data.