Benchmark results, confusion matrices, ROC curves, and classification reports in CSV and JSON — for testing eval dashboards, metric parsers, and leaderboard importers.
A customer-survey response set — 60 respondents rating five statements on a 1–5 Likert scale plus a 0–10 NPS score and a segment. A fixture for testing survey analysis, aggregation, and NPS/CSAT calculations.
A MovieLens-style ratings log — 240 user/item/rating events with Unix timestamps across 50 users and 40 items. A fixture for testing recommender pipelines, collaborative-filtering loaders, and sparse-matrix builders.
A model-evaluation summary in JSON — per-task scores for a fictional model across sentiment, NER, summarization, translation, and QA, each with its metric and sample size. A fixture for testing eval dashboards and leaderboard importers.
A 3-class confusion matrix as CSV — rows are the true class, columns the predicted class, cells the counts. Paired with a JSON twin for testing metric parsers and evaluation visualisers.
The same 3-class confusion matrix as JSON — a labels array plus a nested counts matrix. The structured twin of the CSV, for testing evaluation tooling.
An ROC curve as CSV — decision threshold with the corresponding false-positive and true-positive rates, monotonic from (0,0) to (1,1). A fixture for testing chart tools and AUC calculators.
A per-class classification report in the scikit-learn structure — precision, recall, F1, and support for each class plus accuracy and macro/weighted averages. A fixture for testing metric parsers and report renderers.
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