data science

What's Like-for-Like (L4L) to be certain that only comparable elements are compared. Elements could be products, stores, customer groups, etc. Here, you possibly can read a good explanation of this topic. In the present case, I'll construct...

Abstract datasets are extremely imbalanced, with positive rates below 0.2%. Standard neural networks trained with weighted binary cross-entropy often achieve high ROC-AUC but struggle to discover suspicious transactions under threshold-sensitive metrics. I propose a...

yourself how real machine learning products actually run in major tech corporations or departments? If yes, this text is for you 🙂 Before discussing scalability, please don’t hesitate to read my first article on...

is a god-send for market research. If you must understand interest in a selected term you possibly can just look it up and see the way it’s changing over time. That is the...

forecasting roughly $50 billion in promoting revenue using econometrics, time-series models, and causal inference. When a senior VP asked how confident we ought to be in a number, I couldn’t hand them a...

dominating the AI debate immediately: that AI goes to interchange all of us, that jobs will disappear inside 18 months, that the collapse of the labor market is inevitable. Some say it with...

, I’ve talked quite a bit about Reterival Augmented Generation (RAG). Specifically, I’ve covered the fundamentals of the RAG methodology, in addition to a bunch of relevant concepts, like chunking, embeddings, reranking, and retrieval...

Although continuous variables in real-world datasets provide detailed information, they should not at all times probably the most effective form for modelling and interpretation. That is where variable discretization comes into play. Understanding variable discretization...