Thresholding a Model Using Money (Not Accuracy): The Worse Model That Made More Profit — Python Solution
Higher accuracy can still lose money if your threshold ignores real costs.
This post shows how to choose thresholds using profit/cost, and why a “worse” model can win financially—backed by Python examples and outputs.
https://medium.com/activated-thinker/profit-vs-accuracy-6e4fbde13100
So, what did we learn in last week's lecture?
(1) The bounty log (history of AI)
(2) Symbolic vs subsymbolic (The two schools)
(3) The mechanics of the chase (ML types)
(4) The black box evaluation
Stay tuned for this weeks lecture on traditional ML technologies (k-Means, linear regression, decision trees)
#AI #machinelearning #cowboybebop #HistoryOfAI #modelEvaluation @fiz_karlsruhe @fizise #lecture #KDAI2026
About metrics for measuring agreement on regression on continuous datasets:
Reasons to avoid R² and use RMSE instead: https://feat.engineering/03-Review_of_the_Modeling_Process.html#sec-reg-metrics
From Max Kuhn @topepo, Kjell Johnson (2026), "Feature Engineering and Selection: A Practical Approach for Predictive Models"
#prediction #dataDev #modelEvaluation #regression #modelling #linearRegression #modeling #probability #probabilities #statistics #stats #gotcha
What Is F1 Score in Machine Learning? A Practical Guide
A simple way to balance precision and recall when accuracy is misleading.
This post explains F1 with a clear confusion-matrix view, when it matters (imbalanced classes), and how to interpret trade-offs—plus a small Python example.
#MachineLearning #DataScience #Python #ModelEvaluation #ai #medium #ML
@ai @theartificialintelligence @programming @towardsdatascience
@pythonclcoding @chartrdaily @medium
Probability Calibration with Python
Make model scores behave like real probabilities.
Many classifiers rank well but give bad probabilities (0.9 does not mean “90%”). This post shows how to test calibration (reliability curves, Brier score) and fix it with Platt scaling or isotonic regression in Python.
https://hasanaligultekin.medium.com/probability-calibration-with-python-6ee602760ab6
#MachineLearning #Python #ModelEvaluation #DataScience #MLOps
@programming @ai @towardsdatascience @pythonclcoding
@chartrdaily
Model Selection and Evaluation: A Practical Machine Learning Guide
How to pick a model that stays reliable after deployment.
Offline scores can look great and still fail in production. This post covers strong baselines, clean splits, the right metrics, thresholding, and monitoring—so the model keeps working after it ships.
#MachineLearning #DataScience #ModelEvaluation #MLOps #Python
@medium @towardsdatascience @programming @pythonclcoding @chartrdaily
Manning Publications (@ManningBooks)
추론(reasoning) 모델의 중요성이 장기적으로 큰 변화를 가져온다는 내용입니다. Meta 등 기업들이 추론 모델을 밀고 있으며 VentureBeat가 MobileLLM-R1을 언급했고, @rasbt의 Build를 통해 추론 모델이 실제로 어떻게 구축되고 평가되는지 배울 수 있다는 점을 강조합니다.
AI moves fast, but some shifts matter long after the headlines pass. Reasoning models are one of 'em. As it grows, even companies like @Meta are pushing them, as @VentureBeat highlights with MobileLLM-R1. Want to learn how they're are actually built & evaluated? @rasbt's Build
Q*Satoshi (@AiXsatoshi)
MiniMax-M2.1 모델에 대해 '언어적 일관성 유지에는 다소 어려움이 있다'고 지적한 짤막한 평가성 언급입니다. 모델의 언어 일관성 이슈를 관찰한 코멘트로, 후속 성능 분석이나 개선 논의로 확장될 여지가 있습니다.
OpenAI Tries To Measure Whether AI Reasoning Can Be Trusted
Monitorability gets a real test as OpenAI rolls out new evaluations for chain of thought oversight.https://www.olamnews.com/research-report/3315/monitorability-chain-of-thought-evaluations/
Hasan Ali Gültekin
Harald Sack
Eric Maugendre about data
UKP Lab
sayzard
Olam News