numeric_rule_partition

Inputs

Outputs

from spl import SPLClient

result = SPLClient.embedded().call(
    "splime://@splime.project/case5-validation-20261011/numeric_rule_partition@1",
    kwargs={'dataset': 'replace-with-value', 'target_col': 'replace-with-value'},
    trust=True,
)

Review before running. Public bundles contain executable code. Verify the publisher and exact content hash; a virtual environment isolates dependencies, not the operating system.

Evidence

Evidence-backed concepts

• Numeric rule partition
• Holdout row positions
• Target column
• Fitting transformations
• Selecting rules
• Original training
• Reserve stratified iid external holdout
• Target col

• v128 Oct 20263 downloads

• numeric_rule_experimentReproduce a shallow numeric-rule experiment: reserve an external IID holdout, search and fit on development data using exact public stage versions, evaluate the fixed model, and report holdout errors. Caller supplies a numeric pandas DataFrame and target_col; code executes locally.
• find_rulesSearch 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.
• rule_validateValidate an arbitrary supported classification table. Supply target_col or y; optional exclusions, categorical columns and split/search options.
• rule_prepareValidate a prepared Case5 table and apply the chronological grouped search preset.
• rule_evaluateEvaluate a fixed fitted model on development and independent holdout without changing the selected rules.