{"id":1453,"date":"2022-01-07T03:18:31","date_gmt":"2022-01-07T03:18:31","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/01\/07\/anomaly-detection-with-isolation-forest-and-kernel-density-estimation\/"},"modified":"2022-01-07T03:18:31","modified_gmt":"2022-01-07T03:18:31","slug":"anomaly-detection-with-isolation-forest-and-kernel-density-estimation","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/01\/07\/anomaly-detection-with-isolation-forest-and-kernel-density-estimation\/","title":{"rendered":"Anomaly Detection with Isolation Forest and Kernel Density Estimation"},"content":{"rendered":"
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Anomaly detection is to find data points that deviate from the norm. In other words, those are the points that do not follow expected patterns. Outliers and exceptions are terms used to describe unusual data. Anomaly detection is important in a variety of fields because it gives valuable and actionable insights. An abnormality in an MR imaging scan, for instance, might indicate tumorous region in the brain, while an anomalous readout from a manufacturing plant sensor could indicate a broken component.<\/p>\n

After going through this tutorial, you will be able to:<\/p>\n