Apr 12 · 10 min read · Imagine a student who is, in one very specific sense, the best studier in her class. The teacher hands out a practice test before the real one. Our student takes it home and memorises it. Every question. Every answer. Every typo. If you ask her the t...
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Apr 6 · 6 min read · The ML Model's Tightrope Walk: Balancing Bias and Variance for Peak Performance Introduction: The Goldilocks Problem of Machine Learning Welcome, aspiring data scientists and ML enthusiasts! Ever wondered why some machine learning models perform bril...
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Mar 29 · 15 min read · TLDR: 🎯 Accuracy is a lie when classes are imbalanced. Real ML evaluation uses precision (how many positives are actually positive), recall (how many actual positives we caught), F1 (their balance), and AUC-ROC (performance across all thresholds). T...
Join discussionMar 29 · 16 min read · TLDR: 🎯 Accuracy is a lie when classes are imbalanced. Real ML evaluation uses precision (how many positives are actually positive), recall (how many actual positives we caught), F1 (their balance), and AUC-ROC (performance across all thresholds). T...
Join discussionMar 29 · 18 min read · TLDR: 🎯 Accuracy is a lie when classes are imbalanced. Real ML evaluation uses precision (how many positives are actually positive), recall (how many actual positives we caught), F1 (their balance), and AUC-ROC (performance across all thresholds). T...
Join discussionFeb 3 · 34 min read · I once questioned whether I belonged here and proved it to myself. This week, I taught a machine to think. Not in the sentient, Terminator way, but practically and elegantly, that makes you wonder why anyone ever made predictions manually (for all yo...
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Jan 30 · 10 min read · In the rush to adopt AI and automation, many teams implement human-in-the-loop (HITL) frameworks. They believe that involving a person in the process solves the problems with reliability, quality, and trust. But as we’ve learned from real engineering...
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