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  • All (20931) +237
    • AI (3154) +13
    • DevOps (932) +6
    • Software (11018) +167
    • IT (5778) +50
    • Education (48)
  • Notice
  • All (20931) +237
  • AI (3154) +13
  • DevOps (932) +6
  • Software (11018) +167
  • IT (5778) +50
  • Education (48)
  • Notice
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  • 1 month ago · ai

    Understanding Errors in Machine Learning: Accuracy, Precision, Recall & F1 Score

    Machine Learning Metrics – An Intuitive Guide Machine Learning models are often judged by numbers, but many beginners and even practitioners misunderstand what...

    #machine learning #accuracy #precision #recall #f1-score #confusion matrix #classification metrics #model evaluation
  • 1 month ago · ai

    Code Generation for Ablation Technique — Documentation

    Overview The Ablation Technique for Code Generation is a methodology used to analyze and improve code‑generation models by systematically removing, disabling,...

    #code generation #ablation study #model evaluation #prompt engineering #large language models
  • 1 month ago · ai

    Thinking Tokens Are Not Created Equal: Why Benchmarks Can't Distinguish Between 'Search' and 'Insight' (A PCP Experiment)

    Experiment Overview I’ve been running experiments to understand how different “reasoning” models actually spend their thinking budget. The results suggest that...

    #LLM #reasoning #token budgeting #benchmarks #post correspondence problem #model evaluation
  • 1 month ago · ai

    How We Are Testing Our Agents in Dev

    Testing that your AI agent is performing as expected is not easy. Here are a few strategies we learned the hard way. The post How We Are Testing Our Agents in D...

    #AI agents #testing strategies #model evaluation #agent performance #development workflow
  • 1 month ago · ai

    The End of the Train-Test Split

    Article URL: https://folio.benguzovsky.com/train-test Comments URL: https://news.ycombinator.com/item?id=46149740 Points: 7 Comments: 1...

    #train-test split #machine learning #model evaluation #cross-validation #data science
  • 1 month ago · ai

    Bias–Variance Tradeoff — Visually and Practically Explained (Part 6)

    What Bias Really Means Practical Definition Bias is how wrong your model is on average because it failed to learn the true pattern. High bias occurs when: - Th...

    #bias-variance tradeoff #overfitting #underfitting #machine learning #model evaluation #regularization #production ML
  • 1 month ago · ai

    Why Accuracy Lies — The Metrics That Actually Matter (Part 4)

    !Cover image for Why Accuracy Lies — The Metrics That Actually Matter Part 4https://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,gravity=auto,for...

    #accuracy #machine-learning-metrics #model-evaluation #production-ml #data-science
  • 1 month ago · ai

    Amazon’s bet that AI benchmarks don’t matter

    Rohit Prasad, Amazon's SVP of AGI. This is an excerpt of Sources by Alex Heath, a newsletter about AI and the tech industry, syndicated just for The Verge subs...

    #Amazon #AI benchmarks #model evaluation #AGI #machine learning #industry perspective
  • 1 month ago · ai

    ⚠️ Data Leakage in Machine Learning

    The Silent Accuracy Killer Ruining Real-World ML Systems Part 2 of the ML Engineering Failure Series Most machine learning beginners obsess over model select...

    #data leakage #machine learning #model evaluation #training pipeline #ML engineering #validation accuracy #production models

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