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Found 3 papers, 1 papers with code
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome.
Hyperparameters and Tuning Strategies for Random Forest
In a benchmark study on several datasets, we compare the prediction performance and runtime of tuneRanger with other tuning implementations in R and RF with default hyperparameters.
On the use of Harrell's C for clinical risk prediction via random survival forests
In simulation studies and with the help of two medical data sets we demonstrate that the accuracy of RSF predictions, as measured by Harrell's C, can be improved if the log-rank statistic is replaced by the C index for node splitting.
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