Abstract:This comprehensive analysis delves into the critical, yet often overlooked, challenge of metric selection mismatch with core business objectives, using the fictional but representative case study of MHTECHIN, a mid-sized enterprise software company. Through MHTECHIN’s journey from strategic drift fueled by misaligned metrics to a position of clarity and growth driven by objective-aligned KPIs, we
Introduction Rare event prediction is a critical domain in machine learning (ML) – often encountered in fields like healthcare, finance, cybersecurity, and engineering, where the events of greatest interest (e.g., fraud, disease outbreak, system failure) occur infrequently, sometimes at rates well below 1%. When building models for such targets, a fundamental challenge is evaluating those models
Introduction Data snooping—sometimes called data dredging or p-hacking—is a critical problem in modern machine learning and data science. It refers to the practice of repeatedly using the same dataset during various phases of statistical analysis, feature selection, model selection, or evaluation. This misuse of data undermines the integrity of evaluation metrics, often leading to models