HHemaNinhema-sdet.hashnode.dev·Apr 30 · 3 min readWhy One Metric Is Never Enough to Evaluate Generative AIAs I continue documenting my daily learning, today I focused on model evaluation for Generative AI, specifically understanding ROUGE, BLEU and BERT Score. These metrics are commonly used to evaluate t00
VUVishal Uttam Maneinvishal-uttam-mane-dataset.hashnode.dev·Apr 30 · 5 min readHandling Imbalanced DatasetsIn real-world machine learning applications, perfectly balanced datasets are more the exception than the rule. Whether in fraud detection, medical diagnosis, anomaly detection, or cybersecurity, the n00
SRStephane Royinflexai.hashnode.dev·Apr 27 · 5 min readHow to Benchmark Open-Source Models Before You CommitYou're choosing between Llama 4 Scout 17B, GPT-OSS 120B, and DeepSeek V3.2. The paper numbers look fine across all three. You pick the one that feels right and ship it. Three weeks later it fails on t00
RSRahul Sehrawatinai-zero-to-hero.hashnode.dev·Apr 12 · 10 min readOverfitting, Underfitting, and Why Your Model Lies to YouImagine 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...00
RBRahul Bohrainrahul-bohra.hashnode.dev·Apr 6 · 6 min readThe ML Model's Tightrope Walk: Balancing Bias and Variance for Peak PerformanceThe 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...00
AAAbstract Algorithmsinabstractalgorithms.dev·Mar 29 · 18 min readModel Evaluation Metrics: Precision, Recall, F1-Score, AUC-ROC ExplainedTLDR: 🎯 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...00
AAAbstract Algorithmsinabstractalgorithms.dev·Mar 29 · 16 min readModel Evaluation Metrics: Precision, Recall, F1-Score, AUC-ROC ExplainedTLDR: 🎯 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), 00
AAAbstract Algorithmsinabstractalgorithms.dev·Mar 29 · 16 min readModel Evaluation Metrics: Precision, Recall, F1-Score, AUC-ROC ExplainedTLDR: 🎯 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...00
EFEdidiayo Fakunleinlines-of-code-layers-of-growth.hashnode.dev·Feb 3 · 34 min readNot a Model Maker: My Fourteenth Week with DataraFlowI 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...00
CCapeStartincstechnologyblog.hashnode.dev·Jan 30 · 10 min readIs Your “Human-in-the-Loop” Actually Slowing You Down? Here’s What We LearnedIn 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...00
