#model-evaluation

Machine Learning: The Future of Intelligent Computing

Learn Machine Learning from Scratch with Visual Examples, Python Code, Real-world Applications, and Complete Roadmap

I read 4 research papers on why metrics quietly stop working -from classic image classification to…

In 2019, a team of researchers set out to do something that sounds almost too careful to be interesting: they rebuilt the ImageNet test set…

Understanding the Confusion Matrix in Machine Learning: A Complete Beginner’s Guide

Learn how Accuracy, Precision, Recall, F1-Score, Specificity, and other evaluation metrics work using one simple example.

Clinical AI Is Tested Constantly…But Is It Actually Validated?

Clinical AI is being tested more than ever. The problem is that we are often testing the wrong things.

Sim-to-Real and Evaluation: Does Any of This Actually Work?

⬅️ Main Hub: The End-to-End Robot Learning Pipeline ⬅️ Previous: World Models: How Robots Learn to Practice in Their Own Imagination

BenchLocal: The Local-First Desktop App That Finally Gives LLM Benchmarking a Shared Runtime and a…

One Platform, Every Stage: How Oumi Streamlines the Foundation Model Lifecycle

An End to End Look at Open Source Training, Evaluation, and Deployment Without the Tool Sprawl

Reasoning Agents Still Fail High-Risk Tasks

Making a model better at reasoning can make it worse at one of the most important production behaviors for agents: knowing when not to call…

Why Most AI Evals Fail Before You Write a Single Test

When people talk about AI evals, the conversation usually starts at the same place — You build an agent or a RAG system, create a golden…

The False Positive Paradox: Why a “Positive Result” Is Often Wrong

How rare events (base rates) break our intuition about probability

Project Management in Evaluation of AI in Healthcare — From Research Question to Trustworthy…

Designing a AI Healthcare Project with an appropriate Evaluation Metrics and Quality Assurance Framework

Metrik Evaluasi Regresi: MSE, RMSE, MAE, R², dan MAPE

Kamu baru saja melatih model regresi. Prediksi keluar, angka berderet rapi. Tapi pertanyaan sesungguhnya baru dimulai: seberapa bagus model…

Calculating classification metrics in Power BI

From notebooks to dashboards: Evaluating ML models in Power BI

Healthcare Doesn’t Need Deterministic AI. It Needs Clinically Reproducible AI.

“The challenge with healthcare AI isn’t that it occasionally says things differently. The challenge is ensuring that it consistently…

F1 Score in Named Entity Recognition Tasks — An Intuition

It’s been 10 years now since I’ve stepped into the world of machine learning when I first took such a course at university. One of the…

What I Learned Making a Local LLM Do Real Work

This article was first published on the Open Teams Engineering Blog by Adam Lewis, Senior Software Engineer.

Which 8B Model Should You Actually Ship Your Agent On? I Tested 5 of Them.

A reproducible local benchmark of IBM Granite 4.1, Granite 4.0, Llama 3.1, Qwen 2.5, and Mistral on tool calling — run on a MacBook M3.

From Data to Reliable Models: Why I Wrote Practical Data Modeling and Machine Learning with Python

Building models is easy. Building models that work is not.

Spiral Dynamics Explains How Worldviews Evolve — But Can We Evaluate Them?

From developmental models to system-level testing: what’s missing in how we compare belief systems

Why Medical AI Cannot Recognize What It Does Not Know

A web based diagnostic tool presents a structured form. It asks for symptoms, duration, intensity, and associated signals. The inputs are…

What BLEU Actually Measures — And Why I Started Using It to Validate AI Output

I ran into BLEU while trying to answer a simple question:

When OCR “Wins,” Multimodal Evals Lose

Why screenshot text extraction can fool your benchmarks — and how to evaluate what the model actually understood about the UI.

Building a Debate Engine for Classifier Edge Cases

A technical deep-dive into confidence routing, multi-persona debate modes, and pluggable judge strategies in llm-jury

When Your Classifier Isn’t Sure, Let the Experts Debate

How a research paper on sexism detection led me to build an open-source SDK for classifier edge cases

Machine Learning Series (Part 17): Precision–Recall Curves: How to Choose the Right Threshold

“A picture is worth a thousand words”.

ML Metrics for Ranking: Accuracy, Precision, and Recall at Rank 1,2,3 and so on.

How to Evaluate AI Agent Skills Without Relying on Vibes

A practical, platform-agnostic guide to skill evaluation, from first test case to production pipeline

Post-Training Validation: Cross-Validation vs Bootstrapping

How to evaluate models reliably when data is limited, noisy, or expensive

Open-Source LLMs 2026: Choose, Evaluate & Deploy Self-Hosted Models

Learn how to pick open-source and open-weight LLMs, check licenses, evaluate benchmarks, and deploy privately with confidence for real…

[Duke University] 폐 이식 준비 완료?

배경 및 개요

4 Powerful Metrics For Testing AI Model Performance [2025]

A Guide to Accuracy, Precision, Recall, F1-Score, and AUC-ROC with Real-World Examples

How to Evaluate Your RAG Pipelines with Giskard

A Gentle Guide to RAG Evaluation Using Giskard

旅行時間分析專案系列 — 06: 基線模型建模

建立預測旅行時間的基線模型，評估簡單迴歸與時間序列方法的效果，作為進階建模的參考基準

Grid Search VS Random Search

Hiperparametre optimizasyonu yöntemleri ile model başarısının arttırılması

Calculating mAP of models on Edge device

Presenting a way to calculate mAP on edge devices. It is an important metric for evaluating performance of an object detection model

Machine Learning Train, Test & Model Evaluation Techniques — Easy way!

In this post we will discuss train-test split, k-cross validation, accuracy, precision, recall and F1-Score in a simplified way!