Which is the status of the system?

In this section, the best explainable visualisations to verify the system’s status are displayed. Anomalies are deviations or irregularities in data patterns that stand out from the expected or normal behavior. So it is fundamental to assess and ensure the proper functioning, performance, and integrity of a system.

Confusion Matrix


Example from XMANAI

A confusion matrix is an N x N matrix used for evaluating the performance of a classification model. It summarizes the counts of true positive, true negative, false positive, and false negative predictions. It compares actual target values with those predicted by the machine learning model, providing insights into accuracy and error rates. It is particularly useful for assessing model performance in various scenarios, such as imbalanced datasets, error analysis, adjusting thresholds, and multiclass classification.

This confusion matrix has been used by a XMANAI demonstrator to provides a visualization helping workers gain insights into the correlation between a machine anomalies and the sensors associated to them. Anomalies are categorized and compared to sensor values, with colors indicating the strength of correlation: red for high correlation and blue for low correlation.