Statistics

Feature selection stays one of the vital critical yet computationally expensive steps within the machine learning pipeline. When working with high-dimensional datasets, identifying which features truly contribute to predictive power can mean the difference...

of Green Dashboards Metrics bring order to chaos, or not less than, that’s what we assume. They summarise multi-dimensional behaviour into consumable signals, clicks into conversions, latency into availability and impressions into ROI. Nonetheless,...

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...