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holdout

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Machine-Learning-Toolbox

This dataset was used to learn more about how some machine learning models work: KNN, Naive Bayes, and Decision Tree. It also includes some model evaluation metrics: Precision, Recall, Accuracy, and F1-Score. These metrics were derived from the confusion matrix.

  • Updated Oct 28, 2023
  • Jupyter Notebook

Design and read incrementality / lift tests for mobile game UA, then reconcile conflicting platform, MMP, and SKAN numbers into one defensible ROAS. Built for post-ATT measurement where last-click attribution lies. Dependency-free, runs in the browser.

  • Updated Jun 29, 2026
  • HTML

The held-out validation set for voice-agent-eval-harness: six hand-authored transcripts, their ground-truth labels, and the judged run they were opened against. A separate repository, so holding the set out is a mechanism rather than a promise, with every sealed digest recomputable from the published files.

  • Updated Sep 23, 2026
  • Python

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