Isolation Forest: A Tree-based Algorithm for Anomaly Detection

This is the 10th in a series of small, bite-sized articles I am writing about algorithms that are commonly used in anomaly detection (I’ll put links to all other articles towards the end). In today’s article, I’ll focus on a tree-based machine learning algorithm — Isolation Forest — that can efficiently isolate outliers from a multi-dimensional dataset.

My objective here is to give an intuition of how the algorithm works and how to implement it in a few lines of codes as a demonstration. So I am not going deep into the theory, but just enough to help readers understand the basics. You can always search and lookup for details if there’s a specific part of the algorithm that you are interested. So let’s dive right in!

What is Isolation Forest?

Isolation Forest or iForest is one of the more recent algorithms which was first proposed in 2008 [1] and later published in a paper in 2012 [2]. Around 2016 it was incorporated within the Python Scikit-Learn library.

It is a tree-based algorithm, built around the theory of decision trees and random forests. When presented with a dataset, the algorithm splits the data into two parts based on a random threshold value. This process continues recursively until each data point is isolated. Once the algorithm runs through the whole data, it filters the data points which took fewer steps than others to be isolated. Isolation Forest in sklearn is part of the Ensemble model class, it returns the anomaly score of each instance to measure abnormality.

#isolation-forests #outlier-detection #anomaly-detection #data-science #machine-learning

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Isolation Forest: A Tree-based Algorithm for Anomaly Detection

This is the 10th in a series of small, bite-sized articles I am writing about algorithms that are commonly used in anomaly detection (I’ll put links to all other articles towards the end). In today’s article, I’ll focus on a tree-based machine learning algorithm — Isolation Forest — that can efficiently isolate outliers from a multi-dimensional dataset.

My objective here is to give an intuition of how the algorithm works and how to implement it in a few lines of codes as a demonstration. So I am not going deep into the theory, but just enough to help readers understand the basics. You can always search and lookup for details if there’s a specific part of the algorithm that you are interested. So let’s dive right in!

What is Isolation Forest?

Isolation Forest or iForest is one of the more recent algorithms which was first proposed in 2008 [1] and later published in a paper in 2012 [2]. Around 2016 it was incorporated within the Python Scikit-Learn library.

It is a tree-based algorithm, built around the theory of decision trees and random forests. When presented with a dataset, the algorithm splits the data into two parts based on a random threshold value. This process continues recursively until each data point is isolated. Once the algorithm runs through the whole data, it filters the data points which took fewer steps than others to be isolated. Isolation Forest in sklearn is part of the Ensemble model class, it returns the anomaly score of each instance to measure abnormality.

#isolation-forests #outlier-detection #anomaly-detection #data-science #machine-learning

Isolation Forest is the best Anomaly Detection Algorithm for Big Data Right Now

Isolation forest or “iForest” is an astoundingly beautiful and elegantly simple algorithm that identifies anomalies with few parameters. The original paper  is accessible to a broad audience and contains minimal math. In this article, I will explain why iForest is the best anomaly detection algorithm for big data right now, provide a summary of the algorithm, history of the algorithm and share a code implementation.

Why iForest is the best anomaly detection algorithm for big data right now

Algorithm main points/summary

Algorithm history

Code implementations

…

#data-science #anomaly-detection #machine-learning #outlier-detection #big-data #isolation forest

These Tips Will Help You Step Up Anomaly Detection Using ML

In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two.

A common need when you analyzing real-world data-sets is determining which data point stand out as being different from all other data points. Such data points are known as anomalies, and the goal of anomaly detection (also known as outlier detection) is to determine all such data points in a data-driven fashion. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously unknown, underlying process.

#machine-learning #machine-learning-algorithms #anomaly-detection #detecting-data-anomalies #data-anomalies #machine-learning-use-cases #artificial-intelligence #fraud-detection

Isolation Forest Algorithm for Anomaly Detection

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.

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.

#isolation-forests #heartbeat #anomaly-detection #machine-learning #artificial-intelligence

Introduction to Random Forest Algorithm: Functions, Applications & Benefits

Random Forest is a mainstream AI algorithm that has a place with the regulated learning strategy. It might be used for both Classification and Regression issues in ML. It depends on the idea of ensemble learning, which is a cycle of joining numerous classifiers to tackle an intricate issue and to improve the presentation of the model.

As the name proposes, “Random Forest is a classifier that contains different decision trees on various subsets of the given dataset and takes the typical to improve the perceptive precision of that dataset.”

Instead of relying upon one decision tree, the random forest takes the figure from each tree and subject it to the larger part votes of desires, and it predicts the last yield. The more noticeable number of trees in the forest prompts higher exactness and forestalls the issue of overfitting.

**Presumptions for Random Forest **

Since the random forest consolidates various trees to anticipate the class of the dataset, it is conceivable that some choice trees may foresee the right yield, while others may not. Yet, together, all the trees anticipate the right yield. In this way, beneath are two presumptions for a superior random forest classifier:

• There should be some real qualities in the component variable of a dataset with a goal that the classifier can foresee precise outcomes as opposed to a speculated result.
• The forecasts from each tree must have low connections.

#artificial intelligence #random forest #introduction to random forest algorithm #random forest algorithm #algorithm