Identification of data anomalies by univariate analysis of the values of each feature using models of classical statistics as well as unsupervised machine learning.
Graphical representation of the occurrence of anomalies per feature. Values above the the feature mean are defined as high or low and are color coded.
A univariate graphical analysis of the selected feature. The analysis requires the selection of a numerical feature. A boxplot is shown with the individual values superimposed as a point cloud. Values identified as anomalies are color coded. The corresponding distribution of values is shown as a bar chart.
Table that provides detailed univariate information on the values of the selected feature that are identified as anomalies.
A table summarizing the results of the anomaly detection procedure. For each feature, the absolute number of anomalies is displayed. In addition, the number of instances is listed, as well as the number of anomalies above and below feature mean and the respective fraction of detected anomalies.