Cross-Validation: Why One Train/Test Split Lies

A developer demonstrates why a single train/test split can be misleading in machine learning, advocating for k-fold cross-validation to obtain a more reliable performance estimate. The interactive visualization shows how k-fold cross-validation rotates through folds, providing a mean accuracy with standard deviation, compared to the variance of a single split. The project is part of the MachineLearningFromZero series.

You split your data 80/20, get 91% accuracy, and ship it. But was that 91% luck or skill? A single split can fool you. Cross-validation gives you a trustworthy number. Here's k-fold, visualized. 🔁 Watch the folds rotate: https://dev48v.infy.uk/ml/day18-cross-validation.html https://dev48v.infy.uk/ml/day18-cross-validation.html One train/test split is high-variance: a lucky test set flatters your model, an unlucky one trashes it. You're judging on a single roll of the dice. Split the data into k equal folds. Then, k times: train on k−1 folds, validate on the held-out one. You get k scores — report the mean ± std . Every data point gets used for both training and validation in different rounds , so the estimate is stable. The demo rotates each fold through validation, fits a real model per fold, fills in the per-fold scores, and shows the average — next to a single split you can reshuffle to watch it swing. Cost: k× the training. Worth it for an honest score. 🔨 Built from scratch split into folds → train/score each → mean±std → grid-search on the page: https://dev48v.infy.uk/ml/day18-cross-validation.html https://dev48v.infy.uk/ml/day18-cross-validation.html Part of MachineLearningFromZero. 🌐 https://dev48v.infy.uk https://dev48v.infy.uk