1 research outputs found
Per-sample Prediction Intervals for Extreme Learning Machines
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