Isolation Forest Algorithm for Anomaly Detection

Isolation Forest Algorithm for Anomaly Detection

Anomaly detection is one of the most popular machine learning techniques. In this article, we will learn concepts related to anomaly detection and how to implement it as a machine learning model.

Introduction:

Did you ever wonder how credit card fraud detection is caught in real-time? Do you want to know how to catch an intruder program if it is trying to access your system? This is all possible by the application of the anomaly detection machine learning model.

Anomaly detection is one of the most popular machine learning techniques. In this article, we will learn concepts related to anomaly detection and how to implement it as a machine learning model.

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What is Anomaly Detection?

In simple words, we can define the finding of abnormal events, data, or activity during a process, such as running an app or program, as anomaly detection. In the picture above, only one egg is red and the others are white. The identification of the red color egg is an outcome of anomaly detection, as it is different from the pattern. As we know, generally eggs are white, so the presence of a red egg is violating the pattern.

Let us try to understand this with one more example. As we know, if an egg is floating in the water, it might be old and rotten. This indicates that the weight of eggs varies and, on the basis of its weight, one can differentiate between a fresh egg and a rotten egg.

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