Here you’ll find everything about random isolation forests, whether it’s tutorials on implementing them in Python or conceptual articles.
A Random Isolation Forest is an ensemble machine learning model that serves the purpose of detecting anomalies in a dataset. It is an unsupervised learning algorithm. A random isolation forest works by randomly selecting a feature and a split value for that feature to create a decision tree. The decision tree is then trained on a sample of the data, and the length of the path from the root node to the leaf node that contains a given data point is calculated. In multiple iterations, this process creates a forest of decision trees. Anomalous points will have shorter path lengths on average. As a result, the algorithm can identify them by looking for points with unusually short path lengths.
Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on the fight against international credit … Read more
