How do you "influence" a ML model? For example, imagine a scenario where you'd like to detect anomalies in a given data set. You reach for your favourite algorithm - in my case Isolation Forest: Our example output from Isolation Forest It does fine for most cases, except that one data point which invariably gets … Continue reading Machine Learning: Oversampling vs Sample Weighting
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
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
Introduction In this 3-part series we'll explore how three machine learning algorithms can help a hypothetical financial analyst explore a real data set of credit card transactions to quickly and easily infer relationships, anomalies and extract useful data. Data Set The data set we'll use in this hypothetical scenario is a real data set released … Continue reading Analyzing credit card transactions using machine learning techniques
Maybe the link between your smartphone keyboard and current machine learning research in cybersecurity is not apparent at first glance, but the technology behind both is extremely similar: both leverage deep learning architectures called Recurrent Neural Networks [RNNs], specifically a type of RNN called Long Short Term Memory [LSTM]. One of the main advantages of … Continue reading What do Smartphone Predictive Text and Cybersecurity have in common?
The intersection between machine learning [ML] and information security [InfoSec] is currently quite a hot topic. The allure of this intersection is easy to see, security analysts are drowning in alerts and data which need to be painstakingly investigated and if necessary acted upon. This is no easy processes and as was seen in the … Continue reading First Steps in applying machine learning to InfoSec – WEKA