xsl_holdout_datasets: Registry of datasets held out from xsl_datasets

In XSLmodels: Run and Optimize Cross-Situational Word Learning Models

Registry of datasets held out from xsl_datasets

Description

A named list, one entry per dataset in the package that is not part of xsl_datasets and therefore never seen by get_group_model_fit()/get_crossvalidated_model_fit() – i.e. every dataset available for testing a fitted model's generalization to genuinely held-out data. This is a lookup table (dataset name -> how to score it), not a copy of the data itself – look up the dataset by its own name (e.g. benitez2020, vlach_debrock2017) to use it.

Usage

xsl_holdout_datasets

Format

An object of class list of length 7.

Details

Each entry has:

scorable

Whether the dataset can be scored with the package's standard diagonal convention (xsl_run() + mafc_test()/get_perf()) out of the box.

scoring

How to actually score a model's fit against it.

note

Why it's held out, or any caveat for using it.

Most of these are standalone for a structural reason (partial-NA accuracy, a non-square association matrix, or no human accuracy data at all, e.g. the naturalistic corpora) – see each dataset's own ?help page for the full rationale. benitez2020 and kachergis_initial_accuracy are the exceptions: both are directly scorable the same way xsl_datasets conditions are, and are kept out of the group/CV fitting pool specifically to have real held-out data to test generalization against.

Examples

xsl_holdout_datasets$benitez2020$scoring
names(xsl_holdout_datasets)[sapply(xsl_holdout_datasets, `[[`, "scorable")]

XSLmodels documentation built on Oct. 18, 2026, 5:08 p.m.