How do you interpret and explain the results of anomaly detection with unsupervised learning?

1 What is an anomaly?

An anomaly is an instance that deviates significantly from the normal or expected behavior of the data. Anomalies can be caused by various factors, such as noise, errors, or malicious activities. Depending on the context and the goal of the analysis, anomalies can be either interesting or problematic. For example, an anomaly in a credit card transaction might indicate fraud, while an anomaly in a customer review might indicate a valuable insight. Therefore, it is important to define what constitutes an anomaly and how to measure its severity.

2 How to measure anomaly scores?

One of the common ways to measure anomaly scores is to use the distance or similarity between an instance and the rest of the data. For example, if you use k-means clustering to group the data into clusters, you can compute the distance between each instance and its assigned cluster center. The larger the distance, the more anomalous the instance. Alternatively, you can use density-based methods, such as local outlier factor (LOF), to compute the relative density of each instance compared to its neighbors. The lower the density, the more anomalous the instance.

3 How to set anomaly thresholds?

Once you have computed the anomaly scores for each instance, you need to decide how to separate the normal instances from the anomalous ones. One way to do this is to set a threshold based on some criteria, such as a predefined percentage, a statistical test, or a domain knowledge. For example, you can label the top 1% of the instances with the highest anomaly scores as anomalies, or you can use a z-score test to determine if an instance is significantly different from the mean. However, setting a threshold can be subjective and arbitrary, and it may not capture the complexity and variability of the data.

4 How to evaluate anomaly detection models?

Evaluating anomaly detection models can be challenging, especially when you do not have labeled data or ground truth to compare with. One of the possible ways to evaluate anomaly detection models is to use external validation, which means to compare the results with some other sources of information, such as domain experts, feedback, or historical data. For example, you can ask an expert to review the detected anomalies and provide feedback on their relevance and importance, or you can compare the results with previous records or reports. External validation can help you assess the accuracy and usefulness of your anomaly detection models.

5 How to explain anomaly detection results?

Explaining anomaly detection results can be crucial for gaining trust and understanding from your stakeholders, such as customers, managers, or regulators. One of the possible ways to explain anomaly detection results is to use visualizations, such as charts, graphs, or maps, to show the distribution and characteristics of the anomalies. For example, you can use a scatter plot to show the location and distance of the anomalies from the clusters, or you can use a heatmap to show the frequency and severity of the anomalies over time. Visualizations can help you communicate the patterns and trends of the anomalies in a clear and intuitive way.

6 How to improve anomaly detection results?

Improving anomaly detection results can be done by tuning and optimizing your unsupervised learning models and methods. Some of the possible ways to improve anomaly detection results are to preprocess and clean your data, to select and engineer relevant features, to choose and adjust appropriate parameters, and to combine and compare different models and methods. For example, you can remove outliers or noise from your data, or you can transform or normalize your data to reduce the impact of scale or distribution. You can also experiment with different algorithms, such as k-means, DBSCAN, or isolation forest, and fine-tune their hyperparameters, such as number of clusters, neighborhood size, or contamination ratio. You can also use ensemble methods, such as averaging or voting, to combine the results of multiple models and methods and increase the robustness and reliability of your anomaly detection results.