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find_rules
Search numeric classification rules with a dataset and target column. Skip unsupported columns and return their names and reasons in unusable_columns, alongside the model, predictions and holdout metrics.
case5_first_pd1_model
Retrieve the fitted first_pd1 JSON model, exact rules and input schema embedded in this immutable release.
case5_first_pd1_predict
Apply the fitted first_pd1 rules embedded in this release. Returns application IDs, predicted classes, probabilities and segments. No caller-side model file or retraining is required.
hn_topic_digest
Turn supplied Hacker News records into an explainable HTML digest in one call: normalize records, apply explicit topic rules, and render a reading list. Returns a notebook-safe HTML fragment by default; full_document=True produces a standalone document. No live fetch or bundled dataset. Runs in the caller's Python environment.
render_digest
Render a supplied topic analysis as an escaped responsive HTML fragment with container-scoped CSS, coverage notes, window counts and a reading list. Safe for inline notebook display by default; full_document=True produces a standalone HTML file.
compound_growth_projection
Projects a starting value after a fixed annual compound growth rate and a whole number of years.
analyze_titles
Apply explicit topic rules and compare document shares in adjacent equal-duration windows. Counts describe the supplied snapshot, not all Hacker News activity.
fetch_snapshot
Fetch and normalize a bounded sample of real Hacker News stories through the official API. Explicit feed, item limit, timeout, retries and total time budget. Requires network access and httpx in the caller's environment; returns records and acquisition provenance. No bundled dataset or fallback data.
normalize_stories
Validate Hacker News records, normalize UTC timestamps and HTTP(S) links, and deduplicate by record identity. Input data stays with the caller.
rule_complete_report
Add application-keyed predictions and evaluation partition labels to a completed Case5 training report.
rule_evaluate
Evaluate a fixed fitted model on development and independent holdout without changing the selected rules.
rule_finalize
Produce a JSON model, exact rules, leaderboard, metrics and audit; omit the intermediate training table.
rule_fit
Fit the selected configuration using development data only.
rule_inspect_model
Validate and inspect a caller-supplied JSON rule model, classes, rules and feature schema.
rule_monitor
Monitor coverage and missing values. Pass optional target_col for labelled quality metrics.
rule_predict
Score a caller-supplied validated rule model without retraining or requiring a target.
rule_prepare
Validate a prepared Case5 table and apply the chronological grouped search preset.
rule_search
Compare bounded interpretable structures with preprocessing and feature selection fitted independently within each CV train fold.
rule_split
Create development/holdout/CV partitions from validated state, respecting configured time and group boundaries.
rule_validate
Validate an arbitrary supported classification table. Supply target_col or y; optional exclusions, categorical columns and split/search options.