3 uses for random decision trees / forests you (maybe) didn’t know about

Decision tree forests rightly get a lot of attention due to their robust nature, support for high dimensions and easy decipherability. The most well known uses of decision tree forests are: Classification - given a set of samples with certain features, classify the samples into discrete classes which the model has been trained on. Regression … Continue reading 3 uses for random decision trees / forests you (maybe) didn’t know about

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Analyzing credit card transactions using machine learning techniques – 3

Introduction In a previous article, we explored how PCA can be used to plot credit card transactions into a 2D space, and we proceeded to visually analyse the results. In this article, we take this process one step further and use hierarchical clustering to automate parts of our analysis, making it even easier for our … Continue reading Analyzing credit card transactions using machine learning techniques – 3

Analyzing credit card transactions using machine learning techniques – 2

Principal Component Analysis - Introduction and Data Preperation Principal Component Analysis [PCA] is an unsupervised algorithm which reduces dimensionality and is widely used. A good visual explanation can be found here: http://setosa.io/ev/principal-component-analysis/ As mentioned in our previous article, Correspondence Analysis  works exclusively on categorical data. In contrast, PCA accepts only numerical data. This means our data … Continue reading Analyzing credit card transactions using machine learning techniques – 2

Anomaly detection vs Ransomware

A big part of what we do at CyberSift is anomaly detection. The recent WannaCry attack highlighted the growing threat of ransomware in the security landscape. The WannaCry authors may have made amateur mistakes, and there may be more stealthy and profitable attacks than WannaCry, but the negative impact it has had on Windows users … Continue reading Anomaly detection vs Ransomware