What is overfitting in machine learning? A plain-English guide to why models memorize training data, how to spot it with validation data, overfitting vs underfitting, and 8 proven ways to prevent it.

Hi all! I’m struggling with this specific challenge in my work and I wonder if anyone has a good advice. I’m trying to fit a prediction model in temporal validation framing, i.e 2023-train, 2024-validation, 2025-test. In conventional prediction model workflow I try to escape overfitting a specific functional forms, but what can I do when the train / val / test sets are different in terms of outcomes? I thought about using IPWs for making the train / val look more like the test set but I’m not sure if I can articulate the “propensity” in a meaningful way. What if there’s some kind of macro event that makes the cohorts look different? I also thought about some fancy rolling fit that will allow me to use just slices of the longitudinal outcome and use more updated sets, but it still feels kind of hacky. I bet @f2harrell would preach for moving to bayesian modeling but I’m not sure if I understand why. By the way, it’s a PPO OLM

A great backtest is easy to fake — accidentally. Lookahead bias, overfitting, and phantom fees destroy more live accounts than bad strategies do. Here's the honest methodology every bot builder should follow in 2026.

Welcome to the forum! Here’s the order I’d follow: 1. **Python + basics** : solid Python, Git, SQL, APIs. Most “AI engineer” jobs are software engineering first. 2. **Light math** : just linear algebra, probability and gradients. Learn it as you go, don’t spend months on it up front. 3. **Classic ML** : scikit-learn, train/test splits, overfitting, metrics. Evaluation habits matter more than fancy models. 4. **Deep learning** : PyTorch basics, then transformers. The HF LLM Course is free and covers Transformers, Tokenizers, fine-tuning and more. 5. **Build with LLMs** : RAG, tool calling, agents. The HF Agents Course is a good hands-on start. 6. **Ship it** : deploy a few projects (API, Space, Docker) and add evaluation and monitoring. **What the market wants:** from the job-description analyses I found, most AI engineer roles are LLM apps, RAG, agents, evaluation and production deployment, not model research. **What to skip at first:** deep math proofs, reading lots of papers, training big models from scratch, and learning every framework (pick one stack and go deep). **Rough emphasis:** math (light), ML (medium), deep learning (medium), generative AI (high), software engineering and deployment (high). Best advice: build 2-3 real projects (e.g. a RAG app over your own documents, with evals) and publish them on the Hub/GitHub. That counts more than certificates.

Automate eval design and hillclimb with Claude to enhance app performance and curb overfitting. This blog outlines eval design principles and showcases Claude’s commands to create evaluations mirrorin

If AI is too precise, it may be overfitting by memorizing rather than learning, which has significant implications for the future of work.