Statistics

under uncertainty is a central concern for product teams. Decisions large and small often must be made under time pressure, despite incomplete — and potentially inaccurate — information concerning the problem and solution...

synthetic data generation, we typically create a model for our real (or ‘observed’) data, after which use this model to generate synthetic data. This observed data is often compiled from real world experiences,...

construct a regression model, which implies fitting a straight line on the information to predict future values, we first visualize our data to get an idea of the way it looks and to...

a small NumPy project series where I try to truly with NumPy as an alternative of just going through random functions and documentation. I’ve all the time felt that the most effective...

and managing products, it’s crucial to make sure they’re performing as expected and that the whole lot is running easily. We typically depend on metrics to gauge the health of our products. And...

A previous article provided a of conceptual frameworks – analytical structures for representing abstract concepts and organizing data. Data scientists use such frameworks in a wide range of contexts, from use case ideation and...

discussed about classification metrics like ROC-AUC and Kolmogorov-Smirnov (KS) Statistic in previous blogs. On this blog, we are going to explore one other vital classification metric called the Gini Coefficient. Why do we've multiple classification...

In a seminal but underappreciated book titled , Marcus Hutter attempted a mathematical formulation of universal artificial intelligence, shortened to AIXI. This text goals to make AIXI accessible to data scientists, technical enthusiasts and...