Thank you, @Joel Wilson, for recommending the wonderful LLM runs a shop-video in your community. This forces me to recommend the channel: https://www.youtube.com/@FingoChat/videos

To level up in a Skool community, focus on consistent, high-value engagement: – Post helpful content that sparks discussion or solves a common problem. – Comment meaningfully on other members’ posts to build relationships. – Show up regularly — short daily interactions matter more than rare long ones. – Ask thoughtful questions that invite shared experience and insight. – Support others' progress — give feedback, encouragement, or tools they can use. Skool rewards relevance, reliability, and relational behavior. Be visible for the right. Be the best reason for other members to be here .... Photo credit: You see my picture of „Bayerische Staatsbibliothek, Munich, Germany“ (a wonderful place to stay forever for a day at least.)

Hello, Ashley Oak, Germans Frolovs, Milos Radojcic, Deanna Ouellette, Camila Drego, Mick Holloway, Deana Bishop, Stephen Corpas, Umair Elahi, Tarun Bhardwaj, Future of Work Professor, Abdurrahman Ibrahim, Shawn S, Frances P, Joel Wilson, Mykhailo Sytnyk, Henrik Hansen, Rakib Firoz, Md Rokonuzzaman, Ivan Wong, Eduardo Peiro, Laura Ionescu, Usman Karamat .... Welcome!

Model Evaluation Metrics: Model evaluation metrics quantify machine learning performance through mathematical formulations capturing different aspects of predictive quality, from classification accuracy and regression errors to ranking effectiveness and probabilistic calibration. The engineering challenge involves selecting metrics aligned with business objectives, understanding metric relationships and tradeoffs, computing confidence intervals for statistical significance, handling multi-output and hierarchical predictions, and translating technical metrics into stakeholder-interpretable performance indicators. Model Evaluation Metrics explained for People without AI-Background - Model evaluation metrics are like different ways of grading a student's performance - just as you might measure accuracy (test scores), speed (time to complete), consistency (variation across tests), and improvement (progress over time), different metrics reveal different strengths and weaknesses of machine learning models, with the right metric depending on what matters most for your specific problem. Why Do Different Metrics Capture Distinct Performance Aspects? Different metrics emphasize various error types and prediction characteristics, with no single metric fully characterizing model performance necessitating multi-metric evaluation. Accuracy (correct/total) weights all errors equally becoming meaningless with class imbalance - 99% accurate predicting all negative with 1% positive rate. Mean Squared Error penalizes large errors quadratically making it sensitive to outliers, while Mean Absolute Error treats all errors linearly providing robustness. Logarithmic loss evaluates probability quality beyond hard predictions, heavily penalizing confident wrong predictions essential for risk-sensitive applications. Area Under ROC Curve measures discrimination ability independent of threshold, while precision-recall curves better characterize performance on imbalanced datasets. Each metric's mathematical formulation embeds assumptions about error costs and decision contexts.