  Prediction intervals in supervised Machine Learning bound the region where
the true outputs of new samples may fall. They are necessary in the task of
separating reliable predictions of a trained model from near random guesses,
minimizing the rate of False Positives, and other problem-specific tasks in
applied Machine Learning. Many real problems have heteroscedastic stochastic
outputs, which explains the need of input-dependent prediction intervals.
  This paper proposes to estimate the input-dependent prediction intervals by a
separate Extreme Learning Machine model, using variance of its predictions as a
correction term accounting for the model uncertainty. The variance is estimated
from the model's linear output layer with a weighted Jackknife method. The
methodology is very fast, robust to heteroscedastic outputs, and handles both
extremely large datasets and insufficient amount of training data.
