Efficient Normalized Conformal Prediction and Uncertainty Quantification
  for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests
Daniel Nolte Souparno Ghosh Ranadip Pal
Deep learning models are being adopted and applied on various critical
decision-making tasks, yet they are trained to provide point predictions
without providing degrees of confidence. The trustworthiness of deep learning
models can be increased if paired with uncertainty estimations. Conformal
Prediction has emerged as a promising method to pair machine learning models
with prediction intervals, allowing for a view of the model's uncertainty.
However, popular uncertainty estimation methods for conformal prediction fail
to provide heteroskedastic intervals that are equally accurate for all samples.
In this paper, we propose a method to estimate the uncertainty of each sample
by calculating the variance obtained from a Deep Regression Forest. We show
that the deep regression forest variance improves the efficiency and coverage
of normalized inductive conformal prediction on a drug response prediction
task.