IADT

Provides functions to conduct a model-agnostic asymptotic hypothesis test for the identification of interaction effects in black-box machine learning models. The null hypothesis assumes that a given set of covariates does not contribute to interaction effects in the prediction model. The test statistic is based on the difference of variances of partial dependence functions (Friedman (2008) doi:10.1214/07-AOAS148 and Welchowski (2022) doi:10.1007/s13253-021-00479-7) with respect to the original black-box predictions and the predictions under the null hypothesis. The hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply as the null distribution does not require resampling or refitting black-box prediction models.

Last 30 days

This package has been downloaded 184 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.

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Last 365 days

This package has been downloaded 2,524 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 21, 2024 with 155 downloads.

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