pipelineVerified releasev1
rule_evaluate
Evaluate a fixed fitted model on development and independent holdout without changing the selected rules.
Interface
Verified contract
Inputs
| Name | Type |
|---|---|
| fitted | dict |
Outputs
| Name | Type |
|---|---|
| result | unspecified |
Exact release
Pinned local call
from spl import SPLClient
result = SPLClient.embedded().call(
"splime://@splime.project/case5-validation-20261011/rule_evaluate@1",
kwargs={'fitted': {}},
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
- Rule evaluate
- Changing selected rules
- Independent holdout
- Evaluate fitted model development
- Scikit learn
- Key value mapping
- Numerical arrays
- Tabular analysis
History
Public versions
- v16 downloads
Discover
Similar Objects
- rule_fitFit the selected configuration using development data only.
- rule_splitCreate development/holdout/CV partitions from validated state, respecting configured time and group boundaries.
- rule_inspect_modelValidate and inspect a caller-supplied JSON rule model, classes, rules and feature schema.
- rule_searchCompare bounded interpretable structures with preprocessing and feature selection fitted independently within each CV train fold.
- rule_predictScore a caller-supplied validated rule model without retraining or requiring a target.