Hipatic

Training a Small Classifier Locally: A Practical, Reproducible Workflow Training a Small Classifier Locally: A Practical, Reproducible Workflow This article outlines a minimal, reproducible process for training a small machine-learning classifier on a standard laptop. The objective is to build a functioning model within minutes, using scientifically sound methods and stable tools. Rationale Small models remain the correct baseline for structured data. They train fast, require no GPU, and provide interpretable results. They also establish whether larger architectures are necessary, avoiding premature complexity. Expected Output A clean Python environment A trained classifier using a public dataset AUROC and accuracy metrics A saved model file for later use System Requirements Component Minimum CPU Any modern laptop RAM 4–8 GB Python 3.10 or 3.11 Disk 1 GB free Environment Setup Create the setup file below: cat <<'Eof' setup_cl...