Outlier Detection with the Use of Isolation Forests

Appropriate preparation of data for analysis is a key element in empirical research. Considering the source of data or the nature of the phenomenon studied, some observations may differ significantly from others. Inclusion of such cases in a research may seriously distort the profile of the population under examination. Nevertheless, their omission can be equally disadvantageous. When analyzing dynamically changing phenomena, especially in case of big data, a relatively small amount of outliers may constitute a coherent and internally homogeneous group, which, along with the registration of subsequent observations, may grow into an independent cluster. Whether or not an outlier is removed from the dataset, researcher must be first aware of its existence. For this purpose, an appropriate method of anomaly detection should be used. Identification of such units allows the researcher to make an appropriate decision regarding the further steps in the analysis.