Accelerated oblique random survival forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Oblique RSF ensembles often have higher prediction accuracy than standard RSF ensembles. However, computational overhead and lack of interpretability are pervasive limitations of the oblique RSF, In this talk, I introduce a method to increase computational efficiency of the oblique RSF and a method to estimate importance of individual predictor variables with the oblique RSF. Both methods are available in the aorsf R package.

Accelerated and interpretable oblique random survival forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Oblique RSF ensembles often have higher prediction accuracy than standard RSF ensembles. However, computational overhead and lack of interpretability are pervasive limitations of the oblique RSF, In this talk, I introduce a method to increase computational efficiency of the oblique RSF and a method to estimate importance of individual predictor variables with the oblique RSF. Both methods are available in the aorsf R package.