Random Forests are supervised ensemble-learning models used for classification and regression. Ensemble learning models aggregate multiple machine learning models, allowing for overall better performance. The
Category: Machine Learning
Fraud Detection by Stacking Cost-Sensitive Decision Trees
Recently, we published a research paper showing how it is possible to detect fraudulent credit card transactions with a high level of accuracy and a
Machine Learning Algorithms Explained – Decision Trees
A Decision Tree is a supervised predictive model that can learn to predict discrete or continuous outputs by answering a set of simple questions based
From Real-Time Learning to Reinforcement Learning with Asynchronous Feedback
Online, or real-time, transactional fraud detection systems have recently created quite the buzz in the info security industry. They are an appealing concept: Because we
Building AI Applications Using Deep Learning
Recently, we have seen a huge boom around the field of deep learning; it is currently being implemented in a wide variety of fields, from
Classifying Phishing URLs Using Recurrent Neural Networks
In a recent research paper, we showed how we are able to detect with a high level of accuracy if a website is a phish